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Jabbar, Saba Qasim, and Dheyaa Jasim Kadhim. "A Proposed Adaptive Bitrate Scheme Based on Bandwidth Prediction Algorithm for Smoothly Video Streaming." Journal of Engineering 27, no. 1 (2021): 112–29. http://dx.doi.org/10.31026/j.eng.2021.01.08.
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A robust video-bitrate adaptive scheme at client-aspect plays a significant role in keeping a good quality of video streaming technology experience. Video quality affects the amount of time the video has turned off playing due to the unfilled buffer state. Therefore to maintain a video streaming continuously with smooth bandwidth fluctuation, a video buffer structure based on adapting the video bitrate is considered in this work. Initially, the video buffer structure is formulated as an optimal control-theoretic problem that combines both video bitrate and video buffer feedback signals. While protecting the video buffer occupancy from exceeding the limited operating level can provide continuous video streaming, it may also cause a video bitrate oscillation. So the video buffer structure is adjusted by adding two thresholds as operating points for overflow and underflow states to filter the impact of throughput fluctuation on video buffer occupancy level. Then a bandwidth prediction algorithm is proposed for enhancing the performance of video bitrate adaptation. This algorithm's work depends on the current video buffer level, video bitrate of the previous segment, and iterative throughput measurements to predict the best video bitrate for the next segment. Simulation results show that reserving a bandwidth margin is better in adapting the video bitrate under bandwidth variation and then reducing the risk of video playback freezing. Simulation results proved that the playback freezing happens two times: firstly, when there is no bandwidth margin used and secondly, when the bandwidth margin is high while smooth video bitrate is obtained with moderate value. The proposed scheme is compared with other two schemes such as smoothed throughput rate (STR) and Buffer Based Rate (BBR) in terms of prediction error, QoE preferences, buffer size, and startup delay time, then the proposed scheme outperforms these schemes in attaining smooth video bitrates and continuous video playback.
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Jabbar, Saba Qasim, and Dheyaa Jasim Kadhim. "A Proposed Adaptive Bitrate Scheme Based on Bandwidth Prediction Algorithm for Smoothly Video Streaming." Journal of Engineering 27, no. 1 (2021): 112–29. http://dx.doi.org/10.31026/10.31026/j.eng.2021.01.08.
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A robust video-bitrate adaptive scheme at client-aspect plays a significant role in keeping a good quality of video streaming technology experience. Video quality affects the amount of time the video has turned off playing due to the unfilled buffer state. Therefore to maintain a video streaming continuously with smooth bandwidth fluctuation, a video buffer structure based on adapting the video bitrate is considered in this work. Initially, the video buffer structure is formulated as an optimal control-theoretic problem that combines both video bitrate and video buffer feedback signals. While protecting the video buffer occupancy from exceeding the limited operating level can provide continuous video streaming, it may also cause a video bitrate oscillation. So the video buffer structure is adjusted by adding two thresholds as operating points for overflow and underflow states to filter the impact of throughput fluctuation on video buffer occupancy level. Then a bandwidth prediction algorithm is proposed for enhancing the performance of video bitrate adaptation. This algorithm's work depends on the current video buffer level, video bitrate of the previous segment, and iterative throughput measurements to predict the best video bitrate for the next segment. Simulation results show that reserving a bandwidth margin is better in adapting the video bitrate under bandwidth variation and then reducing the risk of video playback freezing. Simulation results proved that the playback freezing happens two times: firstly, when there is no bandwidth margin used and secondly, when the bandwidth margin is high while smooth video bitrate is obtained with moderate value. The proposed scheme is compared with other two schemes such as smoothed throughput rate (STR) and Buffer Based Rate (BBR) in terms of prediction error, QoE preferences, buffer size, and startup delay time, then the proposed scheme outperforms these schemes in attaining smooth video bitrates and continuous video playback.
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Karagkioules, Theodoros, Georgios S. Paschos, Nikolaos Liakopoulos, Attilio Fiandrotti, Dimitrios Tsilimantos, and Marco Cagnazzo. "Online Learning for Adaptive Video Streaming in Mobile Networks." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 1 (2022): 1–22. http://dx.doi.org/10.1145/3460819.
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In this paper, we propose a novel algorithm for video bitrate adaptation in HTTP Adaptive Streaming (HAS), based on online learning. The proposed algorithm, named Learn2Adapt (L2A) , is shown to provide a robust bitrate adaptation strategy which, unlike most of the state-of-the-art techniques, does not require parameter tuning, channel model assumptions, or application-specific adjustments. These properties make it very suitable for mobile users, who typically experience fast variations in channel characteristics. Experimental results, over real 4G traffic traces, show that L2A improves on the overall Quality of Experience (QoE) and in particular the average streaming bitrate, a result obtained independently of the channel and application scenarios.
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Cwalina, Krzysztof, Slawomir Ambroziak, Piotr Rajchowski, Jaroslaw Sadowski, and Jacek Stefanski. "A Novel Bitrate Adaptation Method for Heterogeneous Wireless Body Area Networks." Applied Sciences 8, no. 7 (2018): 1209. http://dx.doi.org/10.3390/app8071209.
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In the article, a novel bitrate adaptation method for data streams allocation in heterogeneous Wireless Body Area Networks (WBANs) is presented. The efficiency of the proposed algorithm was compared with other known algorithms of data stream allocation using computer simulation. A dedicated simulator has been developed using results of measurements in the real environment. The usage of the proposed adaptive data streams allocation method by transmission rate adaptation based on radio channel parameters can increase the efficiency of resources’ usage in a heterogeneous WBANs, in relation to fixed bitrates transmissions and the use of well-known algorithms. This increase of efficiency has been shown regardless of the mobile node placement on the human body.
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Guo, Jia, Chengrui Li, Jinqi Zhu, et al. "Long Short-Term Memory-Based Non-Uniform Coding Transmission Strategy for a 360-Degree Video." Electronics 13, no. 16 (2024): 3281. http://dx.doi.org/10.3390/electronics13163281.
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This paper studies an LSTM-based adaptive transmission method for a 360-degree video and proposes a non-uniform encoding transmission strategy based on LSTM. Our goal is to maximize the user’s video experience by dynamically dividing the 360-degree video into tiles of different numbers and sizes, and selecting different bitrates for each tile. This aims to reduce buffering events and video jitter. To determine the optimal number and size of tiles at the current moment, we constructed a dual-layer stacked LSTM network model. This model predicts, in real-time, the number, size, and bitrate of the tiles needed for the next moment of the 360-degree video based on the distance between the user’s eyes and the screen. In our experiments, we used an exhaustive algorithm to calculate the optimal tile division and bitrate selection scheme for a 360-degree video under different network conditions, and used this dataset to train our prediction model. Finally, by comparing with other advanced algorithms, we demonstrated the superiority of our proposed method.
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Alahmadi, Mohannad, Peter Pocta, and Hugh Melvin. "An Adaptive Bitrate Switching Algorithm for Speech Applications in Context of WebRTC." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 4 (2021): 1–21. http://dx.doi.org/10.1145/3458751.
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Web Real-Time Communication (WebRTC) combines a set of standards and technologies to enable high-quality audio, video, and auxiliary data exchange in web browsers and mobile applications. It enables peer-to-peer multimedia sessions over IP networks without the need for additional plugins. The Opus codec, which is deployed as the default audio codec for speech and music streaming in WebRTC, supports a wide range of bitrates. This range of bitrates covers narrowband, wideband, and super-wideband up to fullband bandwidths. Users of IP-based telephony always demand high-quality audio. In addition to users’ expectation, their emotional state, content type, and many other psychological factors; network quality of service; and distortions introduced at the end terminals could determine their quality of experience. To measure the quality experienced by the end user for voice transmission service, the E-model standardized in the ITU-T Rec. G.107 (a narrowband version), ITU-T Rec. G.107.1 (a wideband version), and the most recent ITU-T Rec. G.107.2 extension for the super-wideband E-model can be used. In this work, we present a quality of experience model built on the E-model to measure the impact of coding and packet loss to assess the quality perceived by the end user in WebRTC speech applications. Based on the computed Mean Opinion Score, a real-time adaptive codec parameter switching mechanism is used to switch to the most optimum codec bitrate under the present network conditions. We present the evaluation results to show the effectiveness of the proposed approach when compared with the default codec configuration in WebRTC.
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Li, Mao-quan, and Zheng-quan Xu. "An adaptive preprocessing algorithm for low bitrate video coding." Journal of Zhejiang University-SCIENCE A 7, no. 12 (2006): 2057–62. http://dx.doi.org/10.1631/jzus.2006.a2057.
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Peng, Shuai, Jialu Hu, Han Xiao, Shujie Yang, and Changqiao Xu. "Viewport-Driven Adaptive 360◦ Live Streaming Optimization Framework." Journal of Networking and Network Applications 1, no. 4 (2022): 139–49. http://dx.doi.org/10.33969/j-nana.2021.010401.
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Virtual reality (VR) video streaming and 360◦ panoramic video have received extensive attention in recent years, which can bring users an immersive experience. However, the ultra-high bandwidth and ultra-low latency requirements of virtual reality video or 360◦ panoramic video also put tremendous pressure on the carrying capacity of the current network. In fact, since the user’s field of view (a.k.a viewport) is limited when watching a panoramic video and users can only watch about 20%∼30% of the video content, it is not necessary to directly transmit all high-resolution content to the user. Therefore, predicting the user’s future viewing viewport can be crucial for selective streaming and further bitrate decisions. Combined with the tile-based adaptive bitrate (ABR) algorithm for panoramic video, video content within the user’s viewport can be transmitted at a higher resolution, while areas outside the viewport can be transmitted at a lower resolution. This paper mainly proposes a viewport-driven adaptive 360◦ live streaming optimization framework, which combines viewport prediction and ABR algorithm to optimize the transmission of live 360◦ panoramic video. However, existing viewport prediction always suffers from low prediction accuracy and does not support real-time performance. With the advantage of convolutional network (CNN) in image processing and long short-term memory (LSTM) in temporal series processing, we propose an online-updated viewport prediction model called LiveCL which mainly utilizes CNN to extract the spatial characteristics of video frames and LSTM to learn the temporal characteristics of the user’s viewport trajectories. With the help of the viewport prediction and ABR algorithm, unnecessary bandwidth consumption can be effectively reduced. The main contributions of this work include: (1) a framework for 360◦ video transmission is proposed; (2) an online real-time viewport prediction model called LiveCL is proposed to optimize 360◦ video transmission combined with a novel ABR algorithm, which outperforms the existing model. Based on the public 360◦ video dataset, the tile accuracy, recall, precision, and frame accuracy of LiveCL are better than those of the latest model. Combined with related adaptive bitrate algorithms, the proposed viewport prediction model can reduce the transmission bandwidth by about 50%.
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Chen, Jessica, Henry Milner, Ion Stoica, and Jibin Zhan. "Benchmark of Bitrate Adaptation in Video Streaming." Journal of Data and Information Quality 13, no. 4 (2021): 1–24. http://dx.doi.org/10.1145/3468063.
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The HTTP adaptive streaming technique opened the door to cope with the fluctuating network conditions during the streaming process by dynamically adjusting the volume of the future chunks to be downloaded. The bitrate selection in this adjustment inevitably involves the task of predicting the future throughput of a video session, owing to which various heuristic solutions have been explored. The ultimate goal of the present work is to explore the theoretical upper bounds of the QoE that any ABR algorithm can possibly reach, therefore providing an essential step to benchmarking the performance evaluation of ABR algorithms. In our setting, the QoE is defined in terms of a linear combination of the average perceptual quality and the buffering ratio. The optimization problem is proven to be NP-hard when the perceptual quality is defined by chunk size and conditions are given under which the problem becomes polynomially solvable. Enriched by a global lower bound, a pseudo-polynomial time algorithm along the dynamic programming approach is presented. When the minimum buffering is given higher priority over higher perceptual quality, the problem is shown to be also NP-hard, and the above algorithm is simplified and enhanced by a sequence of lower bounds on the completion time of chunk downloading, which, according to our experiment, brings a 36.0% performance improvement in terms of computation time. To handle large amounts of data more efficiently, a polynomial-time algorithm is also introduced to approximate the optimal values when minimum buffering is prioritized. Besides its performance guarantee, this algorithm is shown to reach 99.938% close to the optimal results, while taking only 0.024% of the computation time compared to the exact algorithm in dynamic programming.
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Luo, Dan, Shuhua Xiong, Chao Ren, Raymond Edward Sheriff, and Xiaohai He. "Fusion-Based Versatile Video Coding Intra Prediction Algorithm with Template Matching and Linear Prediction." Sensors 22, no. 16 (2022): 5977. http://dx.doi.org/10.3390/s22165977.
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The new generation video coding standard Versatile Video Coding (VVC) has adopted many novel technologies to improve compression performance, and consequently, remarkable results have been achieved. In practical applications, less data, in terms of bitrate, would reduce the burden of the sensors and improve their performance. Hence, to further enhance the intra compression performance of VVC, we propose a fusion-based intra prediction algorithm in this paper. Specifically, to better predict areas with similar texture information, we propose a fusion-based adaptive template matching method, which directly takes the error between reference and objective templates into account. Furthermore, to better utilize the correlation between reference pixels and the pixels to be predicted, we propose a fusion-based linear prediction method, which can compensate for the deficiency of single linear prediction. We implemented our algorithm on top of the VVC Test Model (VTM) 9.1. When compared with the VVC, our proposed fusion-based algorithm saves a bitrate of 0.89%, 0.84%, and 0.90% on average for the Y, Cb, and Cr components, respectively. In addition, when compared with some other existing works, our algorithm showed superior performance in bitrate savings.
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Guo, Jia, Shiqiang Li, Jinqi Zhu, Xiang Li, Bowen Sun, and Weijia Feng. "Adaptive Transmission Strategy for Non-Uniform Coding of 360∘ Videos." Electronics 13, no. 16 (2024): 3266. http://dx.doi.org/10.3390/electronics13163266.
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A 360° video offers a more immersive experience, gaining increasing popularity among users. However, enhancing the transmission efficiency of 360° videos under limited bandwidth conditions remains a significant challenge. This paper segments the video into three areas: the attention area, the edge area, and the viewpoint-switching transition area. Based on the segmentation of these three distinct video areas, a novel non-uniform coding transmission method for 360° videos is presented, along with mathematical modeling to define the optimization problem. A heuristic algorithm is subsequently introduced, which adaptively determines the optimal number of tiles and allocates the bitrate for each tile in real time to enhance the user’s quality of experience (QoE). Finally, a simulation platform has been developed to validate the efficacy of the proposed algorithm by conducting comparative analyses with existing algorithms.
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Pan, Tung-Ming, Kuo-Chin Fan, and Yuan-Kai Wang. "Object-Based Approach for Adaptive Source Coding of Surveillance Video." Applied Sciences 9, no. 10 (2019): 2003. http://dx.doi.org/10.3390/app9102003.
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Intelligent analysis of surveillance videos over networks requires high recognition accuracy by analyzing good-quality videos that however introduce significant bandwidth requirement. Degraded video quality because of high object dynamics under wireless video transmission induces more critical issues to the success of smart video surveillance. In this paper, an object-based source coding method is proposed to preserve constant quality of video streaming over wireless networks. The inverse relationship between video quality and object dynamics (i.e., decreasing video quality due to the occurrence of large and fast-moving objects) is characterized statistically as a linear model. A regression algorithm that uses robust M-estimator statistics is proposed to construct the linear model with respect to different bitrates. The linear model is applied to predict the bitrate increment required to enhance video quality. A simulated wireless environment is set up to verify the proposed method under different wireless situations. Experiments with real surveillance videos of a variety of object dynamics are conducted to evaluate the performance of the method. Experimental results demonstrate significant improvement of streaming videos relative to both visual and quantitative aspects.
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Saleem, Muhammad, Yasir Saleem, H. M. Shahzad Asif, and M. Saleem Mian. "Quality Enhanced Multimedia Content Delivery for Mobile Cloud with Deep Reinforcement Learning." Wireless Communications and Mobile Computing 2019 (July 18, 2019): 1–15. http://dx.doi.org/10.1155/2019/5038758.
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The importance of multimedia streaming using mobile devices has increased considerably. The dynamic adaptive streaming over HTTP is an efficient scheme for bitrate adaptation in which video is segmented and stored in different quality levels. The multimedia streaming with limited bandwidth and varying network environment for mobile users affects the user quality of experience. We have proposed an adaptive rate control using enhanced Double Deep Q-Learning approach to improve multimedia content delivery by switching quality level according to the network, device, and environment conditions. The proposed algorithm is thoroughly evaluated against state-of-the-art heuristic and learning-based algorithms. The performance metrics such as PSNR, SSIM, quality of experience, rebuffering frequency, and quality variations are evaluated. The results are obtained using real network traces which shows that the proposed algorithm outperforms the other schemes in all considered quality metrics. The proposed algorithm provides faster convergence to the optimal solution as compared to other algorithms considered in our work.
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Zhang, Xi Nan, and Yan Jun Gong. "The Improvement of UMHexagonS Algorithm in AVS Video Coding." Advanced Materials Research 457-458 (January 2012): 819–24. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.819.
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To reduce the complexity of AVS pixel motion vector search, this paper proposes a improved AVS pixel motion estimation UMHexagonS algorithm. The algorithm adds the improve of advanced new array and adaptive adjustment search template. To the video sequence of different motion feature, compare with UMHexagonS algorithm, in the case of mean PSNR descend less than 0.01dB and bitrate only mean increase 0.54％, the time of pixel motion estimation is reduced by 8.72％～20.25％ and the calculated amount of pixel motion estimation is also reduced.
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Dai, Yuqi, Changbin Xue, and Li Zhou. "Visual saliency guided perceptual adaptive quantization based on HEVC intra-coding for planetary images." PLOS ONE 17, no. 2 (2022): e0263729. http://dx.doi.org/10.1371/journal.pone.0263729.
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Due to the limited storage space of spacecraft and downlink bandwidth in the data delivery during planetary exploration, an efficient way for image compression onboard is essential to reduce the volume of acquired data. Applicable for planetary images, this study proposes a perceptual adaptive quantization technique based on Convolutional Neural Network (CNN) and High Efficiency Video Coding (HEVC). This technique is used for bitrate reduction while maintaining the subjective visual quality. The proposed algorithm adaptively determines the Coding Tree Unit (CTU) level Quantization Parameter (QP) values in HEVC intra-coding using the high-level features extracted by CNN. A modified model based on the residual network is exploited to extract the saliency map for a given image automatically. Furthermore, based on the saliency map, a CTU level QP adjustment technique combining global saliency contrast and local saliency perception is exploited to realize a flexible and adaptive bit allocation. Several quantitative performance metrics that efficiently correlate with human perception are used for evaluating image quality. The experimental results reveal that the proposed algorithm achieves better visual quality along with a maximum of 7.17% reduction in the bitrate as compared to the standard HEVC coding.
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Nikolyukin, M. S., and A. D. Obukhov. "Adaptive Processing of Camera Video Stream with Limitations on the Network Data Transmission Bandwidth." Informacionnye Tehnologii 30, no. 5 (2024): 252–60. http://dx.doi.org/10.17587/it.30.252-260.
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Video surveillance systems, cameras, and video stream processing are actively used in many enterprises as a monitoring and control tool for regular and emergency situations, as well as staff activities. The application of intelligent algorithms allows tracking and minimizing operator errors, but these approaches are highly sensitive to the quality of the original video, presence of noise, and low resolution. On the other hand, such video surveillance systems may be limited by network bandwidth. Therefore, this work considers an adaptive video stream processing algorithm that ensures efficient operation of computer vision and object recognition methods while minimizing the amount of transmitted information within network bandwidth constraints. The proposed algorithm addresses the task of determining boundary conditions that ensure the functionality of object recognition algorithms with the least amount of video stream. Corresponding experimental studies were conducted to determine the minimum values of frame resolution and video bitrate. The algorithm was tested in organizing video surveillance at warehouse complexes. The obtained results can be used in developing decision support systems for enterprises in various industries requiring intelligent processing of large volumes of data.
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Kang, Jeongho, and Kwangsue Chung. "HTTP Adaptive Streaming Framework with Online Reinforcement Learning." Applied Sciences 12, no. 15 (2022): 7423. http://dx.doi.org/10.3390/app12157423.
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Dynamic adaptive streaming over HTTP (DASH) is an effective method for improving video streaming’s quality of experience (QoE). However, the majority of existing schemes rely on heuristic algorithms, and the learning-based schemes that have recently emerged also have a problem in that their performance deteriorates in a specific environment. In this study, we propose an adaptive streaming scheme that applies online reinforcement learning. When QoE degradation is confirmed, the proposed scheme adapts to changes in the client’s environment by upgrading the ABR model while performing video streaming. In order to adapt the adaptive bitrate (ABR) model to a changing network environment while performing video streaming, the neural network model is trained with a state-of-the-art reinforcement learning algorithm. The proposed scheme’s performance was evaluated using simulation-based experiments under various network conditions. The experimental results confirmed that the proposed scheme performed better than the existing schemes.
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Hong Lin, Kuei, and Kuo Huang Chung. "A Greedy-Based Video Bitrate Selection Algorithm with Consideration of QoE Fairness for Adaptive Streaming over Software Defined Network." International Journal of Future Computer and Communication 4, no. 2 (2015): 136–42. http://dx.doi.org/10.7763/ijfcc.2015.v4.372.
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Guo, Jia, Yexuan Zhu, Jinqi Zhu, Fan Shen, Hui Gao, and Ye Tian. "Adaptive Streaming Transmission Optimization Method Based on Three-Dimensional Caching Architecture and Environment Awareness in High-Speed Rail." Electronics 13, no. 1 (2023): 41. http://dx.doi.org/10.3390/electronics13010041.
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In high-mobility scenarios, a user’s media experience is severely constrained by the difficulty of network channel prediction, the instability of network quality, and other problems caused by the user’s fast movement, frequent base station handovers, the Doppler effect, etc. To this end, this paper proposes a video adaptive transmission architecture based on three-dimensional caching. In the temporal dimension, video data are cached to different base stations, and in the spatial dimension video data are cached to base stations, high-speed trains, and clients, thus constructing a multilevel caching architecture based on spatio-temporal attributes. Then, this paper mathematically models the media stream transmission process and summarizes the optimization problems that need to be solved. To solve the optimization problem, this paper proposes three optimization algorithms, namely, the placement algorithm based on three-dimensional caching, the video content selection algorithm for caching, and the bitrate selection algorithm. Finally, this paper builds a simulation system, which shows that the scheme proposed in this paper is more suitable for high-speed mobile networks, with better and more stable performance.
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Rahman, Waqas ur, Md Delowar Hossain, and Eui-Nam Huh. "Fuzzy-Based Quality Adaptation Algorithm for Improving QoE from MPEG-DASH Video." Applied Sciences 11, no. 11 (2021): 5270. http://dx.doi.org/10.3390/app11115270.
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Video clients employ HTTP-based adaptive bitrate (ABR) algorithms to optimize users’ quality of experience (QoE). ABR algorithms adopt video quality based on the network conditions during playback. The existing state-of-the-art ABR algorithms ignore the fact that video streaming services deploy segment durations differently in different services, and HTTP clients offer distinct buffer sizes. The existing ABR algorithms use fixed control laws and are designed with predefined client/server settings. As a result, adaptation algorithms fail to achieve optimal performance across a variety of video client settings and QoE objectives. We propose a buffer- and segment-aware fuzzy-based ABR algorithm that selects video rates for future video segments based on segment duration and the client’s buffer size in addition to throughput and playback buffer level. We demonstrate that the proposed algorithm guarantees high QoE across various video player settings and video content characteristics. The proposed algorithm efficiently utilizes bandwidth in order to download high-quality video segments and to guarantee high QoE. The results from our experiments reveal that the proposed adaptation algorithm outperforms state-of-the-art algorithms, providing improvements in average video rate, QoE, and bandwidth utilization, respectively, of 5% to 18%, about 13% to 30%, and up to 45%.
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Chen, Yi, Hongxia Wang, Hanzhou Wu, and Yong Liu. "An adaptive data hiding algorithm with low bitrate growth for H.264/AVC video stream." Multimedia Tools and Applications 77, no. 15 (2017): 20157–75. http://dx.doi.org/10.1007/s11042-017-5411-5.
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Wang, Yimeng, Mridul Agarwal, Tian Lan, and Vaneet Aggarwal. "Learning-Based Online QoE Optimization in Multi-Agent Video Streaming." Algorithms 15, no. 7 (2022): 227. http://dx.doi.org/10.3390/a15070227.
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Video streaming has become a major usage scenario for the Internet. The growing popularity of new applications, such as 4K and 360-degree videos, mandates that network resources must be carefully apportioned among different users in order to achieve the optimal Quality of Experience (QoE) and fairness objectives. This results in a challenging online optimization problem, as networks grow increasingly complex and the relevant QoE objectives are often nonlinear functions. Recently, data-driven approaches, deep Reinforcement Learning (RL) in particular, have been successfully applied to network optimization problems by modeling them as Markov decision processes. However, existing RL algorithms involving multiple agents fail to address nonlinear objective functions on different agents’ rewards. To this end, we leverage MAPG-finite, a policy gradient algorithm designed for multi-agent learning problems with nonlinear objectives. It allows us to optimize bandwidth distributions among multiple agents and to maximize QoE and fairness objectives on video streaming rewards. Implementing the proposed algorithm, we compare the MAPG-finite strategy with a number of baselines, including static, adaptive, and single-agent learning policies. The numerical results show that MAPG-finite significantly outperforms the baseline strategies with respect to different objective functions and in various settings, including both constant and adaptive bitrate videos. Specifically, our MAPG-finite algorithm maximizes QoE by 15.27% and maximizes fairness by 22.47% compared to the standard SARSA algorithm for a 2000 KB/s link.
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Liu, Peng, Jongwon Yoon, Ha Ryung Kim, and Suman Banerjee. "VideoCoreCluster: Energy-Efficient, Low-Cost, and Hardware-Assisted Video Transcoding System." Wireless Communications and Mobile Computing 2018 (June 5, 2018): 1–13. http://dx.doi.org/10.1155/2018/7470234.
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Video streaming is one of the killer applications in recent years. Video transcoding plays an important role in the video streaming service to cope with the various purposes. Specifically, content owners and publishers heavily utilize video transcoders to reconfigure source video in a variety of formats, video qualities, and bitrate to provide end users with the best possible quality of service. In this paper, we present VideoCoreCluster, a low-cost and energy-efficient transcoder cluster that is suitable for live streaming services. We designed and implemented real-time video transcoder cluster using cheap ($35), powerful, and energy-efficient Raspberry Pi. The quality of transcoded video provided by VideoCoreCluster is similar to the best software-based video transcoder while consuming significantly less energy (<3 W). We have proposed a scheduling algorithm based on priority of video stream and transcoding capacity. Our cluster manager provides reliable and scalable streaming services, because it uses the characteristics of adaptive bitrate scheme. We have deployed our transcoding cluster to provide IP-based TV streaming services on our university campus.
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Rodrigues, Frederico, Ivo Sousa, Maria Paula Queluz, and António Rodrigues. "QoE-Aware Scheduling Algorithm for Adaptive HTTP Video Delivery in Wireless Networks." Wireless Communications and Mobile Computing 2018 (September 2, 2018): 1–16. http://dx.doi.org/10.1155/2018/9736360.
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In the last years, the video content consumed by mobile users has increased exponentially. Since mobile network capacity cannot be increased as fast as required, it is crucial to develop intelligent schedulers that allocate radio resources very efficiently and are able to provide a high Quality of Experience (QoE) to most of the users. This paper proposes a new and effective scheduling solution—the Maximum Buffer Filling (MBF) algorithm—which aims to increase the number of satisfied users in video streaming services provided by wireless networks. The MBF algorithm uses the current buffer level at the client side and the radio channel conditions, which are reported to the network by the client, as well as the bitrate of the requested video segment. The proposed scheduling strategy can also fulfill different satisfaction criteria, since it can be tuned to maximize the numbers of users with high QoE levels or to minimize the number of users with low QoE levels. A simulation framework was developed, considering a Long Term Evolution (LTE) scenario, in order to assess the performance of the proposed scheduling scheme and to compare it with other well-known scheduling solutions. The results show the superior performance achieved by the proposed technique, in terms of the number of satisfied and unsatisfied users.
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Patel, Sagar, Sangeetha Abdu Jyothi, and Nina Narodytska. "CrystalBox: Future-Based Explanations for Input-Driven Deep RL Systems." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (2024): 14563–71. http://dx.doi.org/10.1609/aaai.v38i13.29372.
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We present CrystalBox, a novel, model-agnostic, posthoc explainability framework for Deep Reinforcement Learning (DRL) controllers in the large family of input-driven environments which includes computer systems. We combine the natural decomposability of reward functions in input-driven environments with the explanatory power of decomposed returns. We propose an efficient algorithm to generate future-based explanations across both discrete and continuous control environments. Using applications such as adaptive bitrate streaming and congestion control, we demonstrate CrystalBox's capability to generate high-fidelity explanations. We further illustrate its higher utility across three practical use cases: contrastive explanations, network observability, and guided reward design, as opposed to prior explainability techniques that identify salient features.
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A. Abdulhadi, Huda, Dia M. Ali, and Ehab Al-Rawachy. "Advances in Adaptive Filtering for Coherent Dual-Polarization Optical Communication Systems and Their Integration in Dynamic Optical Networks." International Research Journal of Innovations in Engineering and Technology 08, no. 08 (2024): 118–27. http://dx.doi.org/10.47001/irjiet/2024.808014.
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A thorough examination of current developments in adaptive filtering for coherent dualpolarization optical communication systems is provided in this work. The emphasis is on high-capacity networks made possible by dual-polarization, coherent detection, variable bit-rate transceivers. The review explores the effectiveness of different adaptive algorithms in coherent receivers, the importance of dual polarization, and the function of adaptive filters in reducing channel impairments. The research also sheds light on the tradeoffs and difficulties related to flexible bitrate optical transceivers. The paper comprises an extensive assessment of dynamic optical networks, categorized by network granularity and generation, in addition to the study of adaptive filtering. A comprehensive understanding of the developments in dynamic optical networking technologies is provided by the discussion of the evolution and traits of each generation. The poll also covers optical access networks, emphasizing the acceptance and advantages of optical access protocols as IEEE EPON and ITU-T GPON. The study also examines how digital filtering might be used to manage transmission constraints, highlighting the significance of segmenting digital filtering into distinct blocks. An overview of the types and applications of optical filters utilized in optical communication systems is included in the discussion. The study wraps up with a review of the literature that summarizes current research on optical communication systems, such as studies on adaptive algorithms' convergence properties, coherent dual-polarization receivers, and machine learning's use in optical fiber communication. The contributions of this paper include a thorough analysis of adaptive algorithm performance, a comparative study of dynamic optical networks, and a comprehensive overview of recent research in optical communication systems.
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Sima, Qian, Hui Feng, and Bo Hu. "Latitude-Adaptive Integer Bit Allocation for Quantization of Omnidirectional Images." Applied Sciences 14, no. 5 (2024): 1861. http://dx.doi.org/10.3390/app14051861.
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Omnidirectional images have gained significant popularity and drawn great attention nowadays, which poses challenges to omnidirectional image processing in solving the bottleneck of storage and transmission. Projecting onto a two-dimensional image plane is generally used to compress an omnidirectional image. However, the most commonly used projection format, the equirectangular projection (ERP), results in a significant amount of redundant samples in the polar areas, thus incurring extra bitrate and geometric distortion. We derive the optimal latitude-adaptive bit allocation for each image tile. Subsequently, we propose a greedy algorithm for non-negative integer bit allocation (NNIBA) for non-uniform quantization under an omnidirectional image quality metric WMSE. In our experiment, we design quantization tables based on JPEG and compare our approach with other sampling-related methods. Our method achieves an average bit saving of 7.9% compared with JPEG while outperforming other sampling-related methods. Besides, we compare our non-uniform quantization approach with two proposed bit allocation methods, achieving an average improvement of 0.35 dB and 2.66 dB under WS-PSNR, respectively. The visual quality assessment also confirms the superiority of our method.
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Zhang, Weifeng. "Quality Evaluation of Online Mental Health Education Based on Reinforcement Learning in the Pandemic." Discrete Dynamics in Nature and Society 2021 (December 17, 2021): 1–12. http://dx.doi.org/10.1155/2021/7849194.
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The COVID-19 pandemic has become one of the biggest major health crises reported due its massive impact on many countries. From mental health experts, we know that we cannot lose sight of an equally alarming issue which is the long-term mental health impact the pandemic is going to leave on the society. The rapid spread of the pandemic gives little chance to prepare for or even process all that has happened in terms of job losses and the complete uprooting of everyday life and relationships. It is understandable that students may feel irritable, frustrated, or sad sometimes. Loneliness, confusion, and anxiety are also common, but the issue is how we can know if students’ emotions are a normal reaction to an abnormal situation. Therefore, online mental health education has become pretty important for students during the pandemic. Furthermore, it is important to evaluate the quality of online mental health education through microlessons. In this paper, based on Q-learning algorithm, the real-time adaptive bitrate (ABR) configuration parameters mechanism is proposed to detect the changes of network state constantly and select the optimal precalculated configuration according to the current network state. The simulation results show that the proposed algorithm based on Q-learning outperforms other baselines in average latency, average bitrate, and Mean Opinion Score (MOS) on Chrome DevTools and Clumsy. Meanwhile, the experimental results also reveal that the average number of identified mental health problems of the proposed mechanism has always been the best with the bandwidth from 10 Mbit/s to 500 Mbit/s.
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Siddaramappa, Sandeep Gowdra, and Gowdra Shivanandappa Mamatha. "Bit-rate aware effective inter-layer motion prediction using multi-loop encoding structure." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 1 (2025): 569. http://dx.doi.org/10.11591/ijeecs.v37.i1.pp569-579.
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Recently, there has been a notable increase in the use of video content on the internet, leading for the creation of improved codecs like versatile-video-coding (VVC) and high-efficiency video-coding (HEVC). It is important to note that these video coding techniques continue to demonstrate quality degradation and the presence of noise throughout the decoded frames. A number of deep-learning (DL) algorithm-based network structures have been developed by experts to tackle this problem; nevertheless, because many of these solutions use in-loop filtration, extra bits must be sent among the encoding and decoding layers. Moreover, because they used fewer reference frames, they were unable to extract significant features by taking advantage from the temporal connection between frames. Hence, this paper introduces inter-layer motion prediction aware multi-loop video coding (ILMPA-MLVC) techniques. The ILMPA-MLVC first designs an multi-loop adaptive encoder (MLAE) architecture to enhance inter-layer motion prediction and optimization process; second, this work designs multi-loop probabilistic-bitrate aware compression (MLPBAC) model to attain improved bitrate efficiency with minimal overhead; the training of ILMPA-MLVC is done through novel distortion loss function using UVG dataset; the result shows the proposed ILMPA-MLVC attain improved peak-singal-to-noise-ratio (PSNR) and structural similarity (SSIM) performance in comparison with existing video coding techniques.
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Vlaović, Jelena, Drago Žagar, Snježana Rimac-Drlje, and Mario Vranješ. "Evaluation of objective video quality assessment methods on video sequences with different spatial and temporal activity encoded at different spatial resolutions." International journal of electrical and computer engineering systems 12, no. 1 (2021): 1–9. http://dx.doi.org/10.32985/ijeces.12.1.1.
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With the development of Video on Demand applications due to the availability of high-speed internet access, adaptive streaming algorithms have been developing and improving. The focus is on improving user’s Quality of Experience (QoE) and taking it into account as one of the parameters for the adaptation algorithm. Users often experience changing network conditions, so the goal is to ensure stable video playback with satisfying QoE level. Although subjective Video Quality Assessment (VQA) methods provide more accurate results regarding user’s QoE, objective VQA methods cost less and are less time-consuming. In this article, nine different objective VQA methods are compared on a large set of video sequences with various spatial and temporal activities. VQA methods used in this analysis are: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), MultiScale Structural Similarity Index (MS-SSIM), Video Quality Metric (VQM), Mean Sum of Differences (DELTA), Mean Sum of Absolute Differences (MSAD), Mean Squared Error (MSE), Netflix Video Multimethod Assessment Fusion (Netflix VMAF) and Visual Signal-to-Noise Ratio (VSNR). The video sequences used for testing purposes were encoded according to H.264/AVC with twelve different target coding bitrates, at three different spatial resolutions (resulting in a total of 190 sequences). In addition to objective quality assessment, subjective quality assessment was performed for these sequences. All results acquired by objective VQA methods have been compared with subjective Mean Opinion Score (MOS) results using Pearson Linear Correlation Coefficient (PLCC). Measurement results obtained on a large set of video sequences with different spatial resolutions show that VQA methods like SSIM and VQM correlate better with MOS results compared to PSNR, SSIM, VSNR, DELTA, MSE, VMAF and MSAD. However, the PLCC results for SSIM and VQM are too low (0.7799 and 0.7734, respectively), for the usage of these methods in streaming services instead of subjective testing. These results suggest that more efficient VQA methods should be developed to be used in streaming testing procedures as well as to support the video segmentation process. Furthermore, when comparing results obtained for different spatial resolutions, it can be concluded that the quality of video sequences encoded at lower spatial resolutions in cases of lower target coding bitrate is higher compared to the quality of video sequences encoded at higher spatial resolutions at the same target coding bitrate, particularly when video sequences with higher spatial and temporal information are used.
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Viet Hung, Nguyen, Trinh Dac Chien, Nam Pham Ngoc, and Thu Huong Truong. "Flexible HTTP-based Video Adaptive Streaming for good QoE during sudden bandwidth drops." EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 10, no. 2 (2023): e3. http://dx.doi.org/10.4108/eetinis.v10i2.2994.
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We have observed a boom in video streaming over the Internet, especially during the Covid-19 pandemic, that could exceed the network resource availability. In addition to upgrading the network infrastructure, finding a way to smartly adapt the streaming system to the network and users’ conditions to satisfy clients’ perceptions is exceptionally critical, too. This paper proposes a new QoE-aware adaptive streaming scheme over HTTP - ABRA - to make flexible adaptations based on the network and the client’s current status. Besides, we propose a technique that can keep the buffer at an average high for more than 10s. We were limiting the phenomena of rebuffering due to unexpected and unpredictable bandwidth changes. The algorithm keeps the quality of subsequent versions’ quality constant even when the average bitrate decreases, increasing the QoE. Experimental results show that our method can improve QoE from 7.86% to 20.41% compared to state-of-the-art methods. ABRA can provide better performance in terms of QoE score in all buffer conditions compared to the existing solutions while maintaining a minimum secured buffer level for the worst case.
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Nam, Yun Seong, Jianfei Gao, Chandan Bothra, et al. "Xatu." ACM SIGMETRICS Performance Evaluation Review 50, no. 1 (2022): 9–10. http://dx.doi.org/10.1145/3547353.3522641.
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The performance of Adaptive Bitrate (ABR) algorithms for video streaming depends on accurately predicting the download time of video chunks. Existing prediction approaches (i) assume chunk download times are dominated by network throughput; and (ii) apriori cluster sessions (e.g., based on ISP and CDN) and only learn from sessions in the same cluster. We make three contributions. First, through analysis of data from real-world video streaming sessions, we show (i) apriori clustering prevents learning from related clusters; and (ii) factors such as the Time to First Byte (TTFB) are key components of chunk download times but not easily incorporated into existing prediction approaches. Second, we propose Xatu, a new prediction approach that jointly learns a neural network sequence model with an interpretable automatic session clustering method. Xatu learns clustering rules across all sessions it deems relevant, and models sequences with multiple chunk-dependent features (e.g., TTFB) rather than just throughput. Third, evaluations using the above datasets and emulation experiments show that Xatu significantly improves prediction accuracies by 23.8% relative to CS2P (a state-of-the-art predictor). We show Xatu provides substantial performance benefits when integrated with multiple ABR algorithms including MPC (a well studied ABR algorithm), and FuguABR (a recent algorithm using stochastic control) relative to their default predictors (CS2P and a fully connected neural network respectively). Further, Xatu combined with MPC outperforms Pensieve, an ABR based on deep reinforcement learning.
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Nam, Yun Seong, Jianfei Gao, Chandan Bothra, et al. "Xatu: Richer Neural Network Based Prediction for Video Streaming." Proceedings of the ACM on Measurement and Analysis of Computing Systems 5, no. 3 (2021): 1–26. http://dx.doi.org/10.1145/3491056.
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The performance of Adaptive Bitrate (ABR) algorithms for video streaming depends on accurately predicting the download time of video chunks. Existing prediction approaches (i) assume chunk download times are dominated by network throughput; and (ii) apriori cluster sessions (e.g., based on ISP and CDN) and only learn from sessions in the same cluster. We make three contributions. First, through analysis of data from real-world video streaming sessions, we show (i) apriori clustering prevents learning from related clusters; and (ii) factors such as the Time to First Byte (TTFB) are key components of chunk download times but not easily incorporated into existing prediction approaches. Second, we propose Xatu, a new prediction approach that jointly learns a neural network sequence model with an interpretable automatic session clustering method. Xatu learns clustering rules across all sessions it deems relevant, and models sequences with multiple chunk-dependent features (e.g., TTFB) rather than just throughput. Third, evaluations using the above datasets and emulation experiments show that Xatu significantly improves prediction accuracies by 23.8% relative to CS2P (a state-of-the-art predictor). We show Xatu provides substantial performance benefits when integrated with multiple ABR algorithms including MPC (a well studied ABR algorithm), and FuguABR (a recent algorithm using stochastic control) relative to their default predictors (CS2P and a fully connected neural network respectively). Further, Xatu combined with MPC outperforms Pensieve, an ABR based on deep reinforcement learning.
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Li, Xiang, Junfeng Nie, Xinmiao Zhang, et al. "MNCATM: A Multi-Layer Non-Uniform Coding-Based Adaptive Transmission Method for 360° Video." Electronics 13, no. 21 (2024): 4200. http://dx.doi.org/10.3390/electronics13214200.
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With the rapid development of multimedia services and smart devices, 360-degree video has enhanced the user viewing experience, ushering in a new era of immersive human–computer interaction. These technologies are increasingly integrating everyday life, including gaming, education, and healthcare. However, the uneven spatiotemporal distribution of wireless resources presents significant challenges for the transmission of ultra-high-definition 360-degree video streaming. To address this issue, this paper proposes a multi-layer non-uniform coding-based adaptive transmission method for 360° video (MNCATM). This method optimizes video caching and transmission by dividing non-uniform tiles and leveraging users’ dynamic field of view (FoV) information and the multi-bitrate characteristics of video content. First, the video transmission process is formalized and modeled, and an adaptive transmission optimization framework for a non-uniform video is proposed. Based on this, the optimization problem required by the paper is summarized, and an algorithm is proposed to solve the problem. Simulation experiments demonstrate that the proposed method, MNCATM, outperforms existing transmission schemes in terms of bandwidth utilization and user quality of experience (QoE). MNCATM can effectively utilize network bandwidth, reduce latency, improve transmission efficiency, and maximize user experience quality.
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Andrievsky, Boris, Alexander L. Fradkov, and Elena V. Kudryashova. "Control of Two Satellites Relative Motion over the Packet Erasure Communication Channel with Limited Transmission Rate Based on Adaptive Coder." Electronics 9, no. 12 (2020): 2032. http://dx.doi.org/10.3390/electronics9122032.
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The paper deals with the navigation data exchange between two satellites moving in a swarm. It is focused on the reduction of the inter-satellite demanded communication channel capacity taking into account the dynamics of the satellites relative motion and possible erasures in the channel navigation data. The feedback control law is designed ensuring the regulation of the relative satellites motion. The adaptive binary coding/decoding procedure for the satellites navigation data transmission over the limited capacity communication channel is proposed and studied for the cases of ideal and erasure channels. Results of the numerical study of the closed-loop system performance and accuracy of the data transmission algorithm on the communication channel bitrate and erasure probability are obtained by extensive simulations. It is shown that both data transmission error and regulation time depend approximately inversely proportionally on the communication rate. In addition the erasure of data in the channel with probability up to 0.3 does not influence the regulation time for sufficiently high data transmission rate.
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Ma, Linh, Jaehyung Park, Jiseung Nam, HoYong Ryu, and Jinsul Kim. "A Fuzzy-Based Adaptive Streaming Algorithm for Reducing Entropy Rate of DASH Bitrate Fluctuation to Improve Mobile Quality of Service." Entropy 19, no. 9 (2017): 477. http://dx.doi.org/10.3390/e19090477.
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Kim, Kyungmin, and Minseok Song. "Energy-Saving SSD Cache Management for Video Servers with Heterogeneous HDDs." Energies 15, no. 10 (2022): 3633. http://dx.doi.org/10.3390/en15103633.
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Dynamic adaptive streaming over HTTP (DASH) technique, the most popular streaming method, requires a large number of hard disk drives (HDDs) to store multiple bitrate versions of many videos, consuming significant energy. A solid-state drive (SSD) can be used to cache popular videos, thus reducing HDD energy consumption by allowing I/O requests to be handled by an SSD, but this requires effective HDD power management due to limited SSD bandwidth. We propose a new SSD cache management scheme to minimize the energy consumption of a video storage system with heterogeneous HDDs. We first present a technique that caches files with the aim of saving more HDD energy as a result of I/O processing on an SSD. Based on this, we propose a new HDD power management algorithm with the goal of increasing the number of HDDs operated in low-power mode while reflecting the heterogeneous HDD power characteristics. For this purpose, it assigns a separate parameter value to each I/O task based on the ratio of HDD energy to bandwidth and greedily selects the I/O tasks handled by the SSD within limits on its bandwidth. Simulation results show that our scheme consumes between 12% and 25% less power than alternative schemes under the same HDD configuration.
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Garcia, Henrique D., Mylène C. Q. Farias, Ravi Prakash, and Marcelo M. Carvalho. "Statistical characterization of tile decoding time of HEVC-encoded 360° video." Electronic Imaging 2020, no. 9 (2020): 285–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.9.iqsp-285.
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In this paper, we present a statistical characterization of tile decoding time of 360° videos encoded via HEVC that considers different tiling patterns and quality levels (i.e., bitrates). In particular, we present results for probability density function estimation of tile decoding time based on a series of experiments carried out over a set of 360° videos with different spatial and temporal characteristics. Additionally, we investigate the extent to which tile decoding time is correlated with tile bitrate (at chunk level), so that DASH-based video streaming can make possible use of such an information to infer tile decoding time. The results of this work may help in the design of queueing or control theory-based adaptive bitrate (ABR) algorithms for 360° video streaming.
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Roh Bintang Jaya, Mabrur, Widyastuti Andriyani, Domy Kristomo, and Muhammad Agung Nugroho. "Dynamic Bitrate Adjustment in Web-based Video Streaming Applications Using HTTP Live Streaming (HLS)." Journal of Intelligent Software Systems 3, no. 1 (2024): 13. http://dx.doi.org/10.26798/jiss.v3i1.1344.
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This research aims to implement Adaptive Bit Rate (ABR) in the web-based video streaming application JBTV using HTTP Live Streaming (HLS). ABR is a technique that enables automatic adjustment of video bitrate according to user network conditions, while HLS is a streaming protocol that supports adaptive streaming based on HTTP. The research methodology encompasses requirements analysis, system design, implementation, and evaluation. During the requirements analysis phase, the identification of JBTV application requirements and the features needed to implement ABR with HLS were conducted. System design involves the selection of suitable ABR algorithms and the architecture design of the JBTV application that supports HLS. Implementation is carried out by developing the JBTV application capable of generating variant streams with various bitrates and performing adaptive playback according to network conditions
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Zhang, Rui-Xiao, and Tianchi Huang. "Adversarial Attacks on Federated-Learned Adaptive Bitrate Algorithms." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 1 (2024): 419–27. http://dx.doi.org/10.1609/aaai.v38i1.27796.
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Learning-based adaptive bitrate (ABR) algorithms have revolutionized video streaming solutions. With the growing demand for data privacy and the rapid development of mobile devices, federated learning (FL) has emerged as a popular training method for neural ABR algorithms in both academia and industry. However, we have discovered that FL-based ABR models are vulnerable to model-poisoning attacks as local updates remain unseen during global aggregation. In response, we propose MAFL (Malicious ABR model based on Federated Learning) to prove that backdooring the learning-based ABR model via FL is practical. Instead of attacking the global policy, MAFL only targets a single ``target client''. Moreover, the unique challenges brought by deep reinforcement learning (DRL) make the attack even more challenging. To address these challenges, MAFL is designed with a two-stage attacking mechanism. Using two representative attack cases with real-world traces, we show that MAFL significantly degrades the model performance on the target client (i.e., increasing rebuffering penalty by 2x and 5x) with a minimal negative impact on benign clients.
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Wu, Dapeng, Linfeng Cui, Tong Tang, and Ruyan Wang. "Adaptive Bandwidth Prediction and Smoothing Glitches in Low-Latency Live Streaming." Security and Communication Networks 2022 (May 9, 2022): 1–13. http://dx.doi.org/10.1155/2022/4992957.
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HTTP adaptive streaming (HAS) technologies such as dynamic adaptive streaming over HTTP (DASH) and common media application format (CMAF) are now used extensively to deliver live streaming services to large numbers of viewers. However, in dynamic networks, inaccurate bandwidth prediction may result in the wrong request of bitrate, and short-term network fluctuations may produce glitches, causing unnecessary bitrate switching, thereby degrading clients' Quality of Experience (QoE). To tackle this, we propose adaptive bandwidth prediction and smoothing glitches in low-latency live streaming (called APSG) in this article. Concretely, firstly, the size of random bandwidth fluctuations is exploited as the weight of exponentially weighted moving average (EWMA) for adaptive bandwidth prediction; in addition to bandwidth prediction and buffer occupancy, glitches phenomena under a stable network environment are taken into account to enhance the viewing experience of clients. Finally, experimental results show that compared to traditional ABR algorithms under a stable network environment, APSG could reduce the number of bitrate switches and latency by up to 72.6% and 27.3%, respectively; under a dynamic network environment, APSG could reduce the number of bitrate switches and latency by up to 53.8% and 23.6%, respectively.
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Brown, Harrison, Kai Fricke, and Eiko Yoneki. "World-Models for Bitrate Streaming." Applied Sciences 10, no. 19 (2020): 6685. http://dx.doi.org/10.3390/app10196685.
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Adaptive bitrate (ABR) algorithms optimize the quality of streaming experiences for users in client-side video players, especially in unreliable or slow mobile networks. Several rule-based heuristic algorithms can achieve stable performance, but they sometimes fail to properly adapt to changing network conditions. Fluctuating bandwidth may cause algorithms to default to behavior that creates a negative experience for the user. ABR algorithms can be generated with reinforcement learning, a decision-making paradigm in which an agent learns to make optimal choices through interactions with an environment. Training reinforcement learning algorithms for bitrate streaming requires building a simulator for an agent to experience interactions quickly; training an agent in the real environment is infeasible due to the long step times in real environments. This project explores using supervised learning to construct a world-model, or a learned simulator, from recorded interactions. A reinforcement learning agent that is trained inside of the learned model, rather than a simulator, can outperform rule-based heuristics. Furthermore, agents that are trained inside the learned world-model can outperform model-free agents in low sample regimes. This work highlights the potential for world-models to quickly learn simulators, and to be used for generating optimal policies.
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Meng, Zili, Yaning Guo, Yixin Shen, et al. "Practically Deploying Heavyweight Adaptive Bitrate Algorithms With Teacher-Student Learning." IEEE/ACM Transactions on Networking 29, no. 2 (2021): 723–36. http://dx.doi.org/10.1109/tnet.2020.3048666.
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Souane, Naima, Malika Bourenane, and Yassine Douga. "Deep Reinforcement Learning-Based Approach for Video Streaming: Dynamic Adaptive Video Streaming over HTTP." Applied Sciences 13, no. 21 (2023): 11697. http://dx.doi.org/10.3390/app132111697.
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Dynamic adaptive video streaming over HTTP (DASH) plays a crucial role in delivering video across networks. Traditional adaptive bitrate (ABR) algorithms adjust video segment quality based on network conditions and buffer occupancy. However, these algorithms rely on fixed rules, making it challenging to achieve optimal decisions considering the overall context. In this paper, we propose a novel deep-reinforcement-learning-based approach for DASH streaming, with the primary focus of maintaining consistent perceived video quality throughout the streaming session to enhance user experience. Our approach optimizes quality of experience (QoE) by dynamically controlling the quality distance factor between consecutive video segments. We evaluate our approach through a comprehensive simulation model encompassing diverse wireless network environments and various video sequences. We also conduct a comparative analysis with state-of-the-art methods. The experimental results demonstrate significant improvements in QoE, ensuring users enjoy stable, high-quality video streaming sessions.
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Khan, Koffa. "Enhancing Adaptive Video Streaming Through AI-Driven Predictive Analytics for Network Conditions: A Comprehensive Review." International Transactions on Electrical Engineering and Computer Science 3, no. 1 (2024): 57–68. http://dx.doi.org/10.62760/iteecs.3.1.2024.67.
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As the demand for high-quality video streaming continues to surge, the adaptability of streaming systems to dynamic and unpredictable network conditions becomes paramount. This review paper delves into the realm of adaptive video streaming, focusing on the integration of AI-driven predictive analytics to anticipate and optimize network conditions. The paper provides an extensive overview of existing adaptive streaming algorithms, highlighting the challenges posed by fluctuating network conditions. It explores the role of predictive analytics in mitigating these challenges, emphasizing the use of machine learning models and AI technologies. Through case studies and discussions on real-world implementations, the paper showcases how predictive analytics enhances the decision-making process in adaptive streaming systems, leading to improved bitrate adaptation and content delivery. Challenges and limitations associated with predictive analytics are scrutinized, paving the way for a comprehensive understanding of its implications. The integration of predictive analytics into adaptive streaming systems is examined, emphasizing its potential to revolutionize the quality of service. Finally, the paper outlines future trends and research directions, offering insights into the evolving landscape of adaptive video streaming. This review consolidates knowledge and provides a valuable resource for researchers, practitioners, and industry professionals involved in the intersection of video streaming, predictive analytics, and artificial intelligence.
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Park, Jiwoo, Minsu Kim, and Kwangsue Chung. "Buffer-based rate adaptation scheme for HTTP video streaming with consistent quality." Computer Science and Information Systems, no. 00 (2021): 21. http://dx.doi.org/10.2298/csis200820021p.
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Recently, HyperText Transfer Protocol (HTTP) based adaptive streaming (HAS) has been proposed as a solution for efficient use of network resources. HAS performs rate adaptation that adjusts the video quality according to the network conditions. The conventional approaches for rate adaptation involve accurately estimating the available bandwidth or exploiting the playback buffer in HAS clients rather than estimating the network bandwidth. In this paper, we present a playback buffer model for rate adaptation and propose a new buffer-based rate adaptation scheme. First, we model the playback buffer as a queueing system that stores video segments. The proposed scheme selects the next video bitrate that minimizes the difference between the current buffer occupancy and the expected value from the playback buffer model. The evaluation results indicated that the proposed scheme achieves higher video quality than conventional algorithms and can cope with various environments without the tuning of the configuration parameters.
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Liu, Chenghao, Miska M. Hannuksela, and Moncef Gabbouj. "Client-Driven Joint Cache Management and Rate Adaptation for Dynamic Adaptive Streaming over HTTP." International Journal of Digital Multimedia Broadcasting 2013 (2013): 1–16. http://dx.doi.org/10.1155/2013/471683.
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Due to the fact that proxy-driven proxy cache management and the client-driven streaming solution of Dynamic Adaptive Streaming over HTTP (DASH) are two independent processes, some difficulties and challenges arise in media data management at the proxy cache and rate adaptation at the DASH client. This paper presents a novel client-driven joint proxy cache management and DASH rate adaptation method, named CLICRA, which moves prefetching intelligence from the proxy cache to the client. Based on the philosophy of CLICRA, this paper proposes a rate adaptation algorithm, which selects bitrates for the next media segments to be requested by using the predicted buffered media time in the client. CLICRA is realized by conveying information on the segments that are likely to be fetched subsequently to the proxy cache so that it can use the information for prefetching. Simulation results show that the proposed method outperforms the conventional segment-fetch-time-based rate adaptation and the proxy-driven proxy cache management significantly not only in streaming quality at the client but also in bandwidth and storage usage in proxy caches.
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Hoang, Nguyen Huy, Tran Van Nghia, and Le Van Ky. "IMPLEMENTATION OF FPGA-BASED DVB-T2 TRANSMITTER FOR A SECOND GENERATION DIGITAL TERRESTRIAL TELEVISION BROADCASTING SYSTEM." SYNCHROINFO JOURNAL 7, no. 1 (2021): 30–32. http://dx.doi.org/10.36724/2664-066x-2021-7-1-30-32.
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Nowadays, with strong development of Science and Technology, integrated circuits continue to dominate not only in the field of digital information. Over the last several years, Technological television industry has taken huge strides and powerful transformation to meet with government’s policy about digitization of television all over the country in period 2015 – 2020. Stemming from the practical needs of “localization of products” and mastering of technological design of DVB-T2 transmitter (Digital Video Broadcasting – Terrestrial for Second generation), the authors have made an effort to research in algorithm, designed and tested in Field Programmable Gate Array (FPGA) technology. DVB-T2 is mainly aimed to replace the current standard DVB-T. The main motivation of DVB-T2 is to provide broadcasters with more advanced and efficient alternative to DVB-T standards. In DVB-T2 transmitter system, digital audio, video, and other data are compressed into a single signal to be transmitted on a single RF channel, using orthogonal frequency-division multiplexing (OFDM) with concatenated channel coding and interleaving. The higher offered bit rate makes it a suited system for carrying HDTV signals on the terrestrial TV channel. The next generation broadcasting systems should be designed to make full use of spectral resources while providing reliable transmissions in order to enable services like multichannel HDTV (High Definition Television) and innovative data casting services. The efficient usage of the radio spectrum can be achieved by the introduction of Single Frequency Networks (SFN). Digital transmitter DVB-T2 implemented on FPGA using a software Xilinx System Generator for DSP tool and Xilinx ISE Design Suite 14.7. System Generator for DSP is in conjunction on environment MATLAB-Simulink that is capable of simulating the proposed hardware structures that is synthesized and implemented by the programmable elements in Field-programmable Gate Arrays. In this project, adaptative MPEG-TS bitrate converter is designed to allows to increasing or reducing the MPEG TS rate by adding or filtering NULL packets. The entire digital transmitter DVB-T2 is integrated in one chip Xilinx FPGA Kintex-7 XC7K325T-1FFG676. Experimental design on development Kit NetFPGA-1G-CML of Digilent Corporation is performed at design department of technology center of Vietnamese Communications Television Development JSC. Authors are continuing to improve products, put into practical applications to replace the digital terrestrial television broadcasting stations that are being used in Vietnam. The article named “Implementation of FPGA-based DVB-T2 transmitter for a second generation digital terrestrial television broadcasting system” presents the research results, design methods, test results to compare, evaluate the accuracy of algorithm implementation. The results open up new directions for technological television in Vietnam.
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Ye, Jin, Meng Dan, and Wenchao Jiang. "A Visual Sensitivity Aware ABR Algorithm for DASH via Deep Reinforcement Learning." ACM Transactions on Multimedia Computing, Communications, and Applications, June 29, 2023. http://dx.doi.org/10.1145/3591108.
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In order to cope with the fluctuation of network bandwidth and provide smooth video services, adaptive video streaming technology is proposed. In particular, the adaptive bitrate (ABR) algorithm is widely used in dynamic adaptive streaming over HTTP (DASH) to improve quality of experience (QoE). However, existing ABR algorithms still ignore the inherent visual sensitivity of human visual system (HVS). As the final receiver of video, HVS has different sensitivity to the quality distortion of different video content, and video content with high visual sensitivity needs to allocate more bitrate resources. Therefore, existing ABR algorithms still have limitations in reasonably allocating bitrate and maximizing QoE. To solve this problem, this paper designs an adaptive bitrate strategy from the perspective of user vision, studies the modeling of visual sensitivity, and proposes a visual sensitivity aware ABR algorithm. We extract a set of content features and attribute features from the video, and consider the simulation of HVS to establish a total masking effect model that reflects the visual sensitivity more accurately. Further, the network status, buffer occupancy, and visual sensitivity are comprehensively considered under a deep reinforcement learning framework to select the appropriate bitrate for maximizing QoE. We implement the proposed algorithm over a realistic trace-driven evaluation and compare its performance with several latest algorithms. Experimental results show that our algorithm can align ABR strategy with visual sensitivity to achieve better QoE in high visual sensitivity content, and improves the average perceptual video quality and overall user QoE by 18.3% and 22.8% respectively. Additionally, we prove the feasibility of our algorithm through subjective evaluation in the real environment.
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Liu, Daibo, Chao Qian, Huigui Rong, Siwang Zhou, Chaocan Xiang, and Hongbo Jiang. "Energy and QoE Optimization for Mobile Video Streaming with Adaptive Brightness Scaling." ACM Transactions on Sensor Networks, June 12, 2024. http://dx.doi.org/10.1145/3670999.
Texto completoResumen
Brightness scaling (BS) is an emerging and promising technique with outstanding energy efficiency on mobile video streaming. However, existing BS-based approaches totally neglect the inherent interaction effect between BS factor, video bitrate and environment context. Their combined impact on user’s visual perception in mobile scenario, leading to inharmonious between energy consumption and user’s quality of experience (QoE). In this paper, we propose PEO , a novel user- P erception-based video E xperience O ptimization for energy-constrained mobile video streaming, by jointly considering the inherent connection between device’s state of motion, video quality and the resulting user-perceived quality. Specifically, by capturing the motion of on-the-run device, PEO first infers the optimal bitrate and BS factor, therefore avoiding bitrate-inefficiency for energy saving while guaranteeing the user-perceived QoE. On that basis, we formulate the device motion-aware and user perception-aware video streaming as an optimization problem where we present an optimal algorithm to maximize the object function and adapt to user preference, and thus propose an online bitrate selection algorithm. Our evaluation (based on trace analysis and user study) shows that, compared with state-of-the-art techniques, PEO can raise the perceived quality by 23.8%-41.3% and save up to 25.2% energy consumption.