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Journal articles on the topic 'Streaming adaptatif HTTP'

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Published: 25 May 2024

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1

Nguyen, Duc V., Hung T. Le, Pham Ngoc Nam, Anh T. Pham, and Truong Cong Thang. "Adaptation method for video streaming over HTTP/2." IEICE Communications Express 5, no. 3 (2016): 69–73. http://dx.doi.org/10.1587/comex.2015xbl0177.

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2

Le, Hung T., Hai N. Nguyen, Nam Pham Ngoc, Anh T. Pham, and Truong Cong Thang. "A Novel Adaptation Method for HTTP Streaming of VBR Videos over Mobile Networks." Mobile Information Systems 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/2920850.

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Recently, HTTP streaming has become very popular for delivering videos over the Internet. For adaptivity, a provider should generate multiple versions of a video as well as the related metadata. Various adaptation methods have been proposed to support a streaming client in coping with strong bandwidth variations. However, most of existing methods target at constant bitrate (CBR) videos only. In this paper, we present a new method for quality adaptation in on-demand streaming of variable bitrate (VBR) videos. To cope with strong variations of VBR bitrate, we use a local average bitrate as the representative bitrate of a version. A buffer-based algorithm is then proposed to conservatively adapt video quality. Through experiments in the mobile streaming context, we show that our method can provide quality stability as well as buffer stability even under very strong variations of bandwidth and video bitrates.

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3

Evensen, Kristian, Tomas Kupka, Haakon Riiser, Pengpeng Ni, Ragnhild Eg, Carsten Griwodz, and Pål Halvorsen. "Adaptive Media Streaming to Mobile Devices: Challenges, Enhancements, and Recommendations." Advances in Multimedia 2014 (2014): 1–21. http://dx.doi.org/10.1155/2014/805852.

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Video streaming is predicted to become the dominating traffic in mobile broadband networks. At the same time, adaptive HTTP streaming is developing into the preferred way of streaming media over the Internet. In this paper, we evaluate how different components of a streaming system can be optimized when serving content to mobile devices in particular. We first analyze the media traffic from a Norwegian network and media provider. Based on our findings, we outline benefits and challenges for HTTP streaming, on the sender and the receiver side, and we investigate how HTTP-based streaming affects server performance. Furthermore, we discuss various aspects of efficient coding of the video segments from both performance and user perception point of view. The final part of the paper studies efficient adaptation and delivery to mobile devices over wireless networks. We experimentally evaluate and improve adaptation strategies, multilink solutions, and bandwidth prediction techniques. Based on the results from our evaluations, we make recommendations for how an adaptive streaming system should handle mobile devices. Small changes, or simple awareness of how users perceive quality, can often have large effects.

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4

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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5

Zhang, Weizhan, Hao He, Shuyan Ye, Zhiwen Wang, and Qinghua Zheng. "Enhancing QoE for Mobile Users by Environment-Aware HTTP Adaptive Streaming." Sensors 18, no. 11 (October 27, 2018): 3645. http://dx.doi.org/10.3390/s18113645.

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HTTP adaptive streaming (HAS) has become a dominated media streaming paradigm in today’s Internet, which enriches the user’s experience by matching the video quality with the dynamic network conditions. A range of HAS mechanisms have been proposed to enhance the Quality of Experience (QoE). However, existing mechanisms ignore the environmental impact in the QoE evaluation of mobile users, while the popularity of mobile video allows users to watch videos in diversified scenarios. In this paper, we propose an environment-aware HAS scheme that fully concentrates on the different criteria for evaluating video QoE under different environments. Using the advantage of the sensors in mobile phones, the scheme constructs and validates a video QoE model based on environment perception and then designs a model-driven, environment-aware HAS rate adaptation algorithm. We also evaluate the scheme with an environment-aware DASH (Dynamic Adaptive Streaming over HTTP) player in real mobile environments. Compared to the benchmark HAS mechanism, the experimental results demonstrate that our scheme can provide appropriate differentiated rate adaptation for different environments, resulting in a higher QoE.

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6

Tian, Guibin, and Yong Liu. "Towards Agile and Smooth Video Adaptation in HTTP Adaptive Streaming." IEEE/ACM Transactions on Networking 24, no. 4 (August 2016): 2386–99. http://dx.doi.org/10.1109/tnet.2015.2464700.

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7

Kumar, Venkata Phani M., and Sudipta Mahapatra. "Quality of Experience Driven Rate Adaptation for Adaptive HTTP Streaming." IEEE Transactions on Broadcasting 64, no. 2 (June 2018): 602–20. http://dx.doi.org/10.1109/tbc.2018.2799301.

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8

Thang, Truong Cong, Hung T. Le, Anh T. Pham, and Yong Man Ro. "An Evaluation of Bitrate Adaptation Methods for HTTP Live Streaming." IEEE Journal on Selected Areas in Communications 32, no. 4 (April 2014): 693–705. http://dx.doi.org/10.1109/jsac.2014.140403.

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9

Kim, Myoungwoo, and Kwangsue Chung. "A Video Quality Adaptation Algorithm to Improve QoE for HTTP Adaptive Streaming Service." Journal of KIISE 44, no. 1 (January 15, 2017): 95–106. http://dx.doi.org/10.5626/jok.2017.44.1.95.

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10

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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11

Choi, Wangyu, and Jongwon Yoon. "SATE: Providing Stable and Agile Adaptation in HTTP-Based Video Streaming." IEEE Access 7 (2019): 26830–41. http://dx.doi.org/10.1109/access.2019.2901279.

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12

Geng, Lihong, Liang Pan, Yiqiang Sheng, and Zhichuan Guo. "Towards Smooth and High-Quality Bitrate Adaptation for HTTP Adaptive Streaming." TELKOMNIKA (Telecommunication Computing Electronics and Control) 14, no. 3 (September 1, 2016): 904. http://dx.doi.org/10.12928/telkomnika.v14i3.3517.

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13

Chen, Renkang, Zeping Li, Huawei Yang, and Yangshang Xue. "A User Mobility-Based Rate Adaptation Approach for Dynamic HTTP Streaming." Journal of Physics: Conference Series 1684 (November 2020): 012117. http://dx.doi.org/10.1088/1742-6596/1684/1/012117.

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14

Sobhani, Ashkan, Abdulsalam Yassine, and Shervin Shirmohammadi. "A Video Bitrate Adaptation and Prediction Mechanism for HTTP Adaptive Streaming." ACM Transactions on Multimedia Computing, Communications, and Applications 13, no. 2 (May 5, 2017): 1–25. http://dx.doi.org/10.1145/3052822.

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15

Yuan, Hui, Xiaoqian Hu, Junhui Hou, Xuekai Wei, and Sam Kwong. "An Ensemble Rate Adaptation Framework for Dynamic Adaptive Streaming Over HTTP." IEEE Transactions on Broadcasting 66, no. 2 (June 2020): 251–63. http://dx.doi.org/10.1109/tbc.2019.2954074.

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16

Kesavan, Selvaraj, and J. Jayakumar. "Improvement of adaptive HTTP streaming using advanced real-time rate adaptation." Computers & Electrical Engineering 58 (February 2017): 49–66. http://dx.doi.org/10.1016/j.compeleceng.2016.12.019.

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17

Li, Zhi, Xiaoqing Zhu, Joshua Gahm, Rong Pan, Hao Hu, Ali C. Begen, and David Oran. "Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale." IEEE Journal on Selected Areas in Communications 32, no. 4 (April 2014): 719–33. http://dx.doi.org/10.1109/jsac.2014.140405.

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18

Bentaleb, Abdelhak, Bayan Taani, Ali C. Begen, Christian Timmerer, and Roger Zimmermann. "A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP." IEEE Communications Surveys & Tutorials 21, no. 1 (2019): 562–85. http://dx.doi.org/10.1109/comst.2018.2862938.

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19

Kesavan, Selvaraj, and E. Saravana Kumar. "Rate adaptation performance and quality analysis of adaptive HTTP streaming methods." International Journal of Information Technology 12, no. 2 (August 6, 2019): 453–65. http://dx.doi.org/10.1007/s41870-019-00350-6.

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20

Rahman, Waqas ur, and Kwangsue Chung. "SABA: segment and buffer aware rate adaptation algorithm for streaming over HTTP." Multimedia Systems 24, no. 5 (March 21, 2018): 509–29. http://dx.doi.org/10.1007/s00530-018-0588-7.

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21

Ayad, Ibrahim, Youngbin Im, Eric Keller, and Sangtae Ha. "A Practical Evaluation of Rate Adaptation Algorithms in HTTP-based Adaptive Streaming." Computer Networks 133 (March 2018): 90–103. http://dx.doi.org/10.1016/j.comnet.2018.01.019.

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22

Zhou, Chao, Chia-Wen Lin, and Zongming Guo. "mDASH: A Markov Decision-Based Rate Adaptation Approach for Dynamic HTTP Streaming." IEEE Transactions on Multimedia 18, no. 4 (April 2016): 738–51. http://dx.doi.org/10.1109/tmm.2016.2522650.

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23

Kua, Jonathan, Grenville Armitage, and Philip Branch. "A Survey of Rate Adaptation Techniques for Dynamic Adaptive Streaming Over HTTP." IEEE Communications Surveys & Tutorials 19, no. 3 (2017): 1842–66. http://dx.doi.org/10.1109/comst.2017.2685630.

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24

Dubin, Ran, Raffael Shalala, Amit Dvir, Ofir Pele, and Ofer Hadar. "A fair server adaptation algorithm for HTTP adaptive streaming using video complexity." Multimedia Tools and Applications 78, no. 9 (September 21, 2018): 11203–22. http://dx.doi.org/10.1007/s11042-018-6615-z.

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25

Hoßfeld, Tobias, Michael Seufert, Christian Sieber, Thomas Zinner, and Phuoc Tran-Gia. "Identifying QoE optimal adaptation of HTTP adaptive streaming based on subjective studies." Computer Networks 81 (April 2015): 320–32. http://dx.doi.org/10.1016/j.comnet.2015.02.015.

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26

Liu, Chenghao, Imed Bouazizi, Miska M. Hannuksela, and Moncef Gabbouj. "Rate adaptation for dynamic adaptive streaming over HTTP in content distribution network." Signal Processing: Image Communication 27, no. 4 (April 2012): 288–311. http://dx.doi.org/10.1016/j.image.2011.10.001.

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27

Gohar, Ali, and Sanghwan Lee. "Multipath Dynamic Adaptive Streaming over HTTP Using Scalable Video Coding in Software Defined Networking." Applied Sciences 10, no. 21 (October 30, 2020): 7691. http://dx.doi.org/10.3390/app10217691.

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Dynamic Adaptive Streaming over HTTP (DASH) offers adaptive and dynamic multimedia streaming solutions to heterogeneous end systems. However, it still faces many challenges in determining an appropriate rate adaptation technique to provide the best quality of experience (QoE) to the end systems. Most of the suggested approaches rely on servers or client-side heuristics to improve multimedia streaming QoE. Moreover, using evolving technologies such as Software Defined Networking (SDN) that provide a network overview, combined with Multipath Transmission Control Protocol (MPTCP), can enhance the QoE of streaming multimedia media based on scalable video coding (SVC). Therefore, we enhance our previous work and propose a Dynamic Multi Path Finder (DMPF) scheduler that determines optimal techniques to enhance QoE. DMPF scheduler is a part of the DMPF Scheduler Module (DSM) which runs as an application over the SDN controller. The DMPF scheduler accommodates maximum client requests while providing the basic representation of the media requested. We evaluate our implementation on real network topology and explore how SVC layers should be transferred over network topology. We also test the scheduler for network bandwidth usage. Through extensive simulations, we show clear trade-offs between the number of accommodated requests and the quality of the streaming. We conclude that it is better to schedule the layers of a request into the same path as much as possible than into multiple paths. Furthermore, these result would help service providers optimize their services.

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28

Nguyen, Thoa, Thang Vu, Nam Pham Ngoc, and Truong Cong Thang. "SDP-Based Quality Adaptation and Performance Prediction in Adaptive Streaming of VBR Videos." Advances in Multimedia 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/7323681.

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Recently, various adaptation methods have been proposed to cope with throughput fluctuations in HTTP adaptive streaming (HAS). However, these methods have mostly focused on constant bitrate (CBR) videos. Moreover, most of them are qualitative in the sense that performance metrics could only be obtained after a streaming session. In this paper, we propose a new adaptation method for streaming variable bitrate (VBR) videos using stochastic dynamic programming (SDP). With this approach, the system should have a probabilistic characterization along with the definition of a cost function that is minimized by a control strategy. Our solution is based on a new statistical model where the future streaming performance is directly related to the past bandwidth statistics. We develop mathematical models to predict and develop simulation models to measure the average performance of the adaptation policy. The experimental results show that the prediction models can provide accurate performance prediction which is useful in planning adaptation policy and that our proposed adaptation method outperforms the existing ones in terms of average quality and average quality switch.

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29

Nguyen, Nghia, Long Luu, Phuong Vo, Sang Nguyen, Cuong Do, and Ngoc-Thanh Nguyen. "Reinforcement learning - based adaptation and scheduling methods for multi-source DASH." Computer Science and Information Systems, no. 00 (2022): 55. http://dx.doi.org/10.2298/csis220927055n.

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Dynamic adaptive streaming over HTTP (DASH) has been widely used in video streaming recently. In DASH, the client downloads video chunks in order from a server. The rate adaptation function at the video client enhances the user?s quality-of-experience (QoE) by choosing a suitable quality level for each video chunk to download based on the network condition. Today networks such as content delivery networks, edge caching networks, content centric networks, etc. usually replicate video contents on multiple cache nodes. We study video streaming from multiple sources in this work. In multi-source streaming, video chunks may arrive out of order due to different conditions of the network paths. Hence, to guarantee a high QoE, the video client needs not only rate adaptation, but also chunk scheduling. Reinforcement learning (RL) has emerged as the state-of-the-art control method in various fields in recent years. This paper proposes two algorithms for streaming from multiple sources: RL-based adaptation with greedy scheduling (RLAGS) and RL-based adaptation and scheduling (RLAS). We also build a simulation environment for training and evaluation. The efficiency of the proposed algorithms is proved via extensive simulations with real-trace data.

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30

Kim, Minsu, Heekwang Kim, and Kwangsue Chung. "Quality Adaptation Scheme Based on Stability to Improve QoE of HTTP Adaptive Streaming in Wireless Networks." Journal of KIISE 46, no. 3 (March 31, 2019): 268–76. http://dx.doi.org/10.5626/jok.2019.46.3.268.

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31

LE, Hung T., Nam PHAM NGOC, Anh T. PHAM, and Truong Cong THANG. "A Probabilistic Adaptation Method for HTTP Low-Delay Live Streaming over Mobile Networks." IEICE Transactions on Information and Systems E100.D, no. 2 (2017): 379–83. http://dx.doi.org/10.1587/transinf.2016edl8172.

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32

Ahmad, Hasnah, Navrati Saxena, Abhishek Roy, and Pradipta De. "3B-ARA: Bandwidth, Buffer, and Battery Aware Rate Adaptation for Dynamic HTTP Streaming." IEEE Communications Letters 22, no. 5 (May 2018): 962–65. http://dx.doi.org/10.1109/lcomm.2018.2799878.

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33

Yuan, Hui, Huayong Fu, Ju Liu, Junhui Hou, and Sam Kwong. "Non-Cooperative Game Theory Based Rate Adaptation for Dynamic Video Streaming over HTTP." IEEE Transactions on Mobile Computing 17, no. 10 (October 1, 2018): 2334–48. http://dx.doi.org/10.1109/tmc.2018.2800749.

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34

Hu, Shenghong, Min Xu, Haimin Zhang, Chunxia Xiao, and Chao Gui. "Affective Content-aware Adaptation Scheme on QoE Optimization of Adaptive Streaming over HTTP." ACM Transactions on Multimedia Computing, Communications, and Applications 15, no. 3s (January 22, 2020): 1–18. http://dx.doi.org/10.1145/3328997.

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35

Kesavan, Selvaraj, and J. Jayakumar. "Effective client-driven three-level rate adaptation (TLRA) approach for adaptive HTTP streaming." Multimedia Tools and Applications 77, no. 7 (May 9, 2017): 8081–114. http://dx.doi.org/10.1007/s11042-017-4705-y.

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36

Santos, Carlos Eduardo Maffini, Carlos Alexandre Gouvea da Silva, and Carlos Marcelo Pedroso. "Improving Perceived Quality of Live Adaptative Video Streaming." Entropy 23, no. 8 (July 25, 2021): 948. http://dx.doi.org/10.3390/e23080948.

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Quality of service (QoS) requirements for live streaming are most required for video-on-demand (VoD), where they are more sensitive to variations in delay, jitter, and packet loss. Dynamic Adaptive Streaming over HTTP (DASH) is the most popular technology for live streaming and VoD, where it has been massively deployed on the Internet. DASH is an over-the-top application using unmanaged networks to distribute content with the best possible quality. Widely, it uses large reception buffers in order to keep a seamless playback for VoD applications. However, the use of large buffers in live streaming services is not allowed because of the induced delay. Hence, network congestion caused by insufficient queues could decrease the user-perceived video quality. Active Queue Management (AQM) arises as an alternative to control the congestion in a router’s queue, pressing the TCP traffic sources to reduce their transmission rate when it detects incipient congestion. As a consequence, the DASH client tends to decrease the quality of the streamed video. In this article, we evaluate the performance of recent AQM strategies for real-time adaptive video streaming and propose a new AQM algorithm using Long Short-Term Memory (LSTM) neural networks to improve the user-perceived video quality. The LSTM forecast the trend of queue delay to allow earlier packet discard in order to avoid the network congestion. The results show that the proposed method outperforms the competing AQM algorithms, mainly in scenarios where there are congested networks.

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37

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 (January 31, 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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38

Yan, Zhisheng, Jingteng Xue, and Chang Wen Chen. "Prius: Hybrid Edge Cloud and Client Adaptation for HTTP Adaptive Streaming in Cellular Networks." IEEE Transactions on Circuits and Systems for Video Technology 27, no. 1 (January 2017): 209–22. http://dx.doi.org/10.1109/tcsvt.2016.2539827.

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39

Dubin, Ran, Amit Dvir, Ofir Pele, Ofer Hadar, Itay Katz, and Ori Mashiach. "Adaptation logic for HTTP dynamic adaptive streaming using geo-predictive crowdsourcing for mobile users." Multimedia Systems 24, no. 1 (August 1, 2016): 19–31. http://dx.doi.org/10.1007/s00530-016-0525-6.

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40

Khan, Koffka, and Wayne Goodridge. "Comparative study of One-Shot Learning in Dynamic Adaptive Streaming over HTTP : A Taxonomy-Based Analysis." International Journal of Advanced Networking and Applications 15, no. 01 (2023): 5822–30. http://dx.doi.org/10.35444/ijana.2023.15112.

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Dynamic Adaptive Streaming over HTTP (DASH) has revolutionized multimedia content delivery, enabling efficient video streaming over the internet. One-shot learning, a machine learning paradigm that allows recognition of new classes or objects with minimal training examples, holds promise for enhancing DASH systems. In this comparative study, we present a taxonomy-based analysis of one-shot learning techniques in the context of DASH, examining four taxonomies to provide a comprehensive understanding of their applications, evaluation metrics, and datasets. The first taxonomy focuses on categorizing one-shot learning techniques, including siamese networks, metric learning approaches, prototype-based methods, and generative models. This taxonomy reveals the diversity of techniques employed to tackle one-shot learning challenges in DASH environments. The second taxonomy explores the applications of one-shot learning in DASH. It highlights areas such as video quality prediction, buffer management, content adaptation, and bandwidth estimation, shedding light on how one-shot learning can optimize streaming decisions based on limited or single examples. The third taxonomy addresses evaluation metrics for one-shot learning in DASH. It encompasses accuracy-based metrics, generalization metrics, latency-related metrics, and robustness metrics, providing insights into the performance and effectiveness of one-shot learning approaches under various evaluation criteria. The fourth taxonomy delves into dataset characteristics for one-shot learning in DASH. It categorizes datasets into synthetic datasets, real-world datasets, transfer learning datasets, and unconstrained datasets, enabling researchers to select appropriate data sources and evaluate one-shot learning techniques in diverse streaming scenarios. By conducting this taxonomy-based analysis, our study provides researchers and practitioners with a structured framework for understanding and comparing different aspects of one-shot learning in DASH. It highlights the strengths, weaknesses, and potential applications of various techniques, offers guidance on evaluation metrics, and showcases dataset characteristics for benchmarking and future research. Ultimately, this comparative study aims to foster progress in one-shot learning for DASH by facilitating knowledge exchange, inspiring new research directions, and promoting the development of efficient and adaptive multimedia streaming systems over HTTP.

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41

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 (December 31, 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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42

Ortiz T., Duvernei, Gabriel E. Chanchí G., José L. Arciniegas H., Diego F. Durán D., and Wilmar Y. Campo M. "Escenario para la transmisión de streaming adaptativo DASH-WebM." Revista Colombiana de Computación 18, no. 1 (June 1, 2017): 27–45. http://dx.doi.org/10.29375/25392115.3196.

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Tradicionalmente el videostreaming ha sido soportado por los protocolos RTP y RTSP, de modo que el servidor gestiona una sesión diferente para cada cliente y coordina la entrega de paquetes. Actualmente el estándar de streaming adaptativo DASH ofrece otro enfoque a través de HTTP, de tal forma que el cliente extrae los datos del servidor, sin mantener el estado de la sesión. Así, se tiene como ventajas el pleno uso de la infraestructura de Internet y la adaptación del contenido multimedia al ancho de banda de la red. A pesar de lo anterior, el proceso de generación y distribución de contenidos multimedia DASH, requiere la ejecución secuencial de tareas de codificación, segmentación, creación del descriptor MPD y reproducción del contenido DASH. Para el caso de los contenidos multimedia WebM, las anteriores tareas son realizadas separadamente por un conjunto de herramientas libres, por lo que el proceso de generación del contenido DASH no es automático. En este artículo se propone un escenario de transmisión para streaming adaptativo DASH, cuyo principal componente es la herramienta DASHWebMConverter, la cual se encarga de automatizar el proceso de generación de contenidos DASH WebM. Adicionalmente, se presenta la evaluación del escenario de streaming adaptativo, mediante pruebas de seguimiento de ancho de banda y pruebas de consumo de memoria sobre los principales componentes del escenario.

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Du, Haipeng, Qinghua Zheng, Weizhan Zhang, and Xiang Gao. "A Bandwidth Variation Pattern-Differentiated Rate Adaptation for HTTP Adaptive Streaming Over an LTE Cellular Network." IEEE Access 6 (2018): 9554–69. http://dx.doi.org/10.1109/access.2017.2788057.

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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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Markiewicz, Przemyslaw, and Sławomir Przyłucki. "Influence of video content type on the usefulness of reinforcement learning algorithms in DASH systems." Journal of Computer Sciences Institute 27 (June 30, 2023): 162–70. http://dx.doi.org/10.35784/jcsi.3579.

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The article presents the result of research on DASH (Dynamic Adaptive Streaming over HTTP) systems. In the proposed solution, the adaptive algorithm is based on the RL (Reinforcement Learning) paradigm. The Pensieve algorithm was chosen as the basis for the tests. This algorithm is widely discussed in the scientific literature and therefore the study and analysis of its properties is useful in a wide range of solutions using DASH. The main contribution of the presented test results to the development of knowledge on video streaming services consists in the analysis of the impact of the characteristics of video materials on the effectiveness of the adaptation process implemented by the developed RL model. The presented results show that this influence should not be omitted in any in-depth analyses of the characteristics of DASH systems.

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Nafeh, Majd, Arash Bozorgchenani, and Daniele Tarchi. "Joint Scalable Video Coding and Transcoding Solutions for Fog-Computing-Assisted DASH Video Applications." Future Internet 14, no. 9 (September 17, 2022): 268. http://dx.doi.org/10.3390/fi14090268.

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Video streaming solutions have increased their importance in the last decade, enabling video on demand (VoD) services. Among several innovative services, 5G and Beyond 5G (B5G) systems consider the possibility of providing VoD-based solutions for surveillance applications, citizen information and e-tourism applications, to name a few. Although the majority of the implemented solutions resort to a centralized cloud-based approach, the interest in edge/fog-based approaches is increasing. Fog-based VoD services result in fulfilling the stringent low-latency requirement of 5G and B5G networks. In the following, by resorting to the Dynamic Adaptive Streaming over HTTP (DASH) technique, we design a video-segment deployment algorithm for streaming services in a fog computing environment. In particular, by exploiting the inherent adaptation of the DASH approach, we embed in the system a joint transcoding and scalable video coding (SVC) approach able to deploy at run-time the video segments upon the user’s request. With this in mind, two algorithms have been developed aiming at maximizing the marginal gain with respect to a pre-defined delay threshold and enabling video quality downgrade for faster video deployment. Numerical results demonstrate that by effectively mapping the video segments, it is possible to minimize the streaming latency while maximising the users’ target video quality.

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Seufert, Michael, Nikolas Wehner, and Pedro Casas. "A Fair Share for All: TCP-Inspired Adaptation Logic for QoE Fairness Among Heterogeneous HTTP Adaptive Video Streaming Clients." IEEE Transactions on Network and Service Management 16, no. 2 (June 2019): 475–88. http://dx.doi.org/10.1109/tnsm.2019.2910380.

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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 (June 6, 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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Campo, Wilmar Yesid, Andrés Felipe Escobar Zapata, and Juan Carlos Imbachi Paz. "Análisis del servicio de video streaming basado en el algoritmo FDASH sobre LTE." Ciencia e Ingeniería Neogranadina 29, no. 1 (August 23, 2019): 67–80. http://dx.doi.org/10.18359/rcin.3122.

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El video streaming es el servicio que mayor porcentaje de tráfco genera con respecto altotal de datos en las redes móviles de evolución de largo plazo LTE (por sus siglas en inglés). Por otraparte, el protocolo de Streaming Adaptativo Dinámico sobre HTPP (DASH, por sus siglas en inglés)ha sido seleccionado para la transmisión del video en las redes LTE. Así, en este artículo, se presentael análisis del comportamiento del servicio de video mediante una implementación de DASH bajológica difusa denominada FDASH (fuzzy DASH) en una red LTE, considerando diferente número deusuarios y traspasos (handover). Se analizan parámetros de calidad de servicio, como retardo, pérdida de paquetes, variación de pérdida de paquetes y rendimiento. Mediante este trabajo, se presentaFDASH como opción para el consumo del servicio de video, ya que permite adaptarse a las diferentescondiciones y exigencias de la tecnología inalámbrica móvil LTE.

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Vlaović, Jelena, Snježana Rimac-Drlje, and Drago Žagar. "Content Dependent Representation Selection Model for Systems Based on MPEG DASH." Electronics 10, no. 15 (July 31, 2021): 1843. http://dx.doi.org/10.3390/electronics10151843.

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A standard called MPEG Dynamic Adaptive Streaming over HTTP (MPEG DASH) ensures the interoperability between different streaming services and the highest possible video quality in changing network conditions. The solutions described in the available literature that focus on video segmentation are mostly proprietary, use a high amount of computational power, lack the methodology, model notation, information needed for reproduction, or do not consider the spatial and temporal activity of video sequences. This paper presents a new model for selecting optimal parameters and number of representations for video encoding and segmentation, based on a measure of the spatial and temporal activity of the video content. The model was developed for the H.264 encoder, using Structural Similarity Index Measure (SSIM) objective metrics as well as Spatial Information (SI) and Temporal Information (TI) as measures of video spatial and temporal activity. The methodology that we used to develop the mathematical model is also presented in detail so that it can be applied to adapt the mathematical model to another type of an encoder or a set of encoding parameters. The efficiency of the segmentation made by the proposed model was tested using the Basic Adaptation algorithm (BAA) and Segment Aware Rate Adaptation (SARA) algorithm as well as two different network scenarios. In comparison to the segmentation available in the relevant literature, the segmentation based on the proposed model obtains better SSIM values in 92% of cases and subjective testing showed that it achieves better results in 83.3% of cases.

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