Littérature scientifique sur le sujet « Adaptive bitrate Algorithm »

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.

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.

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.

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.

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.

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.

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%.

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.

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.

La demande de diffusion sans interruption de contenu vidéo et de haute qualité avec une latence minimale est essentielle dans les applications telles que la diffusion sportive et le contrôle de systèmes à distance. Cependant, la diffusion vidéo reste exposée à des défis en raison des caractéristiques variables des canaux de communication, qui peuvent avoir un impact sur la qualité de l'expérience en termes de qualité vidéo et de latence de bout en bout (le temps entre l'acquisition de la vidéo à l'émetteur et son affichage au récepteur).L'objectif de cette thèse est d'aborder le problème des applications en temps réel avec transmission unicast du serveur au client, telles que les applications de contrôle à distance, tout en maintenant une bonne qualité. Nous testons l'efficacité d'une technique récente d'apprentissage profond pour la compensation de la latence dans le schéma de transmission vidéo et son impact sur la qualité. Cette technique prédit les images futures à l'aide des images précédentes disponibles, ce qui permet d'afficher les images au moment voulu. Les résultats montrent que l'extrapolation est prometteuse, en particulier pour les contenus avec peu d'informations temporelles. Cependant, elle doit encore être améliorée en termes de qualité, de prédiction à long terme et de délai d'extrapolation.Plusieurs études se concentrent sur l'intégration d'un système hybride numérique-analogique pour améliorer la qualité perceptive, profitant des avantages des méthodes numériques et analogiques. Nous étudions l'efficacité d'un schéma hybride à faible latence en termes de réduction de la latence tout en maintenant une qualité vidéo élevée. Les résultats montrent que le système hybride améliore la qualité de la vidéo reçue dans la plupart des cas. Cependant, les artefacts d'extrapolation surpassent les artefacts d'encodage et masquent les avantages des schémas hybrides. Ainsi, l'amélioration des performances des schémas hybrides repose sur l'amélioration de l'extrapolation.En plus, les méthodes de diffusion adaptative HTTP ont prouvé leur efficacité pour améliorer la qualité de l'expérience en ajustant le débit d'encodage en fonction des conditions du canal. La plupart de ces algorithmes sont utilisés au client, ce qui pose des problèmes pour répondre aux exigences de latence des applications en temps réel. Dans ces applications, les vidéos sont acquises, compressées et transmises à partir de dispositifs jouant le rôle de serveurs. Donc, ces méthodes pilotées par le client ne conviennent pas à cause de la variabilité des conditions du canal. En plus, la prise de décision se fait avec une périodicité de l'ordre de la seconde, ce qui n'est pas assez réactif lorsque le serveur se déplace, ce qui entraîne des retards importants. Il est donc important d'utiliser une granularité d'adaptation plus fine. Nous visons à contrôler la latence de bout en bout tout en garantissant une qualité d'expérience élevée. Un contrôle du débit d'encodage au niveau d'image à l'émetteur est combiné à une extrapolation au récepteur pour compenser le retard de bout en bout. Le contrôle du débit au niveau d'image permet au système de s'adapter aux variations soudaines des conditions du canal. Un retard apparent de bout en bout nul peut être atteint au prix d'une perte de qualité du signal. Les algorithmes existants tentent d'optimiser les sources individuelles de retard dans le schéma de diffusion vidéo, mais pas de réduire la latence de bout en bout et d'atteindre une latence nulle. Un «Model Predictive Control» impliquant le niveau de mémoire tampon à l'émetteur et l'estimation du débit canal est utilisée pour trouver la valeur optimale du débit d'encodage pour chaque image. Il ajuste dynamiquement le compromis entre le débit de codage et l'horizon d'extrapolation, tout en prévoyant l'impact de la décision relative au débit d'encodage sur les images futures, pour améliorer la qualité d'expérience
The demand for seamless, high-quality video content delivery with minimal latency is paramount in today's applications such as sports broadcasting, videoconferencing, and remote system control. However, video delivery still faces challenges due to unpredictable nature of communication channels. The variations in channel characteristics can impact the quality of experience in terms of content quality and End-To-End latency - the time elapsed between video acquisition at the transmitter and its display at the receiver.The aim of this thesis is to address the issue of real time applications with unicast transmission from server to client such as remote control applications, while maintaining a good quality. We test the effectiveness of a recent deep learning technique for latency compensation in the video transmission scheme and its impact on video quality. This technique predicts future frames using available previous frames, allowing the end-user to display the images at the desired time. The results demonstrate the promise of extrapolation, especially for content with low temporal information. However, it still needs to be improved in terms of quality, long-term prediction, and extrapolation delay.Various studies focus on the integration of a hybrid digital-analog scheme to improve the perceptual quality, taking advantage of the strengths of both digital and analog methods. We study the effectiveness of low-latency hybrid scheme in term of reducing latency while maintaining high video quality. The results show that the hybrid scheme improves the quality of the received video in most cases. However, the extrapolation artifacts outweigh encoding artifacts and mask the advantages of hybrid schemes. Thus, the improvement in hybrid scheme performance relies on the enhancement of extrapolation.Moreover, HTTP Adaptive Streaming methods have proven their effectiveness in improving the quality of experience by dynamically adjusting the encoding rate based on channel conditions. However, most of these adaptation algorithms are implemented at the client level, which poses challenges in meeting latency requirements for real time applications. In addition, in real time application, videos are acquired, compressed, and transmitted from the device acting as the server. Therefore, client-driven rate adaptation approaches are not suitable due to the variability of the channel characteristics. Moreover, in these methods, the decision-making is done with a periodicity of the order of a second, which is not reactive enough when the server is moving, leading to significant delays. Therefore, it is important to use a finer adaptation granularity in order to reduce the End-To-End delay. We aim to control the End-To-End latency during video delivery while ensuring a high quality of experience. A frame-level encoder rate control at the transmitter side is combined with a frame extrapolation at the receiver side to compensate the End-To-End delays. Frame-level rate control enables the system to adapt to sudden variations of channel characteristics. Null apparent End-To-End delay can be reached at the price of some signal quality. To the best of our knowledge, state-of-the-art algorithms try to optimize the individual sources of delay in the video delivery scheme, but not to reduce the whole End-To-End latency and achieve zero latency. A model predictive control approach involving the buffer level at the transmitter and the throughput estimation is used to find the optimal value of encoding rate for each frame. It dynamically adjusts the trade-off between the encoding rate and the extrapolation horizon at the receiver, while predicting the impact of the encoding rate decision on future frames, thus providing the best quality of experience

Attualmente, la maggior parte dei dati che transitano sulla rete appartiene a contenuti multimediali. Più nello specifico, è lo Streaming Video ad avere la predominanza nella condivisione di Internet; vista la crescita che tale servizio ha subìto negli ultimi anni, si sono susseguiti diversi studi volti allo sviluppo di tecniche e metodologie che potessero migliorarlo. Una di queste è sicuramente l'Adaptive Video Streaming, tecnica utilizzata per garantire all'utente una buona Quality of Experience (QoE) mediante l'utilizzo dei cosiddetti "algoritmi di rate adaptation". Il lavoro svolto in questi studi si è voluto concentrare su due filoni distinti, ma allo stesso tempo confrontabili: la prima parte della tesi riguarda lo sviluppo e l'analisi di alcuni algoritmi di rate adaptation per DASH, mentre la seconda è relativa all'implementazione di un nuovo algoritmo che li possa affiancare, migliorando la QoE nel monitorare lo stato della connessione. Si è quindi dovuta implementare un'applicazione Android per lo streaming video, che fosse conforme allo standard MPEG-DASH e potesse fornire le informazioni di testing da utilizzare per le analisi. La tesi è suddivisa in quattro capitoli: il primo introduce l'argomento e definisce la terminologia necessaria alla comprensione degli studi; il secondo descrive alcuni dei lavori correlati allo streaming adattivo e introduce i due filoni principali della tesi, ovvero gli algoritmi di rate adaptation e la proposta di algoritmo per la selezione dinamica del segmento; il terzo presenta l'app SSDash, utilizzata come mezzo per le analisi sperimentali; infine, il quarto ed ultimo capitolo mostra i risultati delle analisi e le corrispondenti valutazioni.

[ES] Esta tesis presenta diversas propuestas encaminadas a mejorar la transmisión de vídeo a través del estándar DASH (Dynamic Adaptive Streaming over HTTP). Este trabajo de investigación estudia el protocolo de transmisión DASH y sus características. A la vez, plantea la codificación con calidad constante y bitrate variable como modo de codificación del contenido de vídeo más indicado para la transmisión de contenido bajo demanda mediante el estándar DASH. Derivado de la propuesta de utilización del modo de codificación de calidad constante, cobra mayor importancia el papel que juegan los algoritmos de adaptación en la experiencia de los usuarios al consumir el contenido multimedia. En este sentido, esta tesis presenta un algoritmo de adaptación denominado Look Ahead el cual, sin modificar el estándar, permite utilizar la información de los tamaños de los segmentos de vídeo incluida en los contenedores multimedia para evitar tomar decisiones de adaptación que desemboquen en paradas no deseadas en la reproducción de contenido multimedia. Con el objetivo de evaluar las posibles mejoras del algoritmo de adaptación presentado, se proponen tres modelos de evaluación objetiva de la QoE. Los modelos propuestos permiten predecir de forma sencilla la QoE que tendrían los usuarios de forma objetiva, utilizando parámetros conocidos como el bitrate medio, el PSNR (Peak Signal-to-Noise Ratio) y el valor de VMAF (Video Multimethod Assessment Fusion). Todos ellos aplicados a cada segmento. Finalmente, se estudia el comportamiento de DASH en entornos Wi-Fi con alta densidad de usuarios. En este contexto, se producen un número elevado de paradas en la reproducción por una mala estimación de la tasa de transferencia disponible debida al patrón ON/OFF de descarga de DASH y a la variabilidad del acceso al medio de Wi-Fi. Para paliar esta situación, se propone un servicio de coordinación basado en la tecnología SAND (MPEG's Server and Network Assisted DASH) que proporciona una estimación de la tasa de transferencia basada en la información del estado de los players de los clientes.
[CA] Aquesta tesi presenta diverses propostes encaminades a millorar la transmissió de vídeo a través de l'estàndard DASH (Dynamic Adaptive Streaming over HTTP). Aquest treball de recerca estudia el protocol de transmissió DASH i les seves característiques. Alhora, planteja la codificació amb qualitat constant i bitrate variable com a manera de codificació del contingut de vídeo més indicada per a la transmissió de contingut sota demanda mitjançant l'estàndard DASH. Derivat de la proposta d'utilització de la manera de codificació de qualitat constant, cobra major importància el paper que juguen els algorismes d'adaptació en l'experiència dels usuaris en consumir el contingut. En aquest sentit, aquesta tesi presenta un algoritme d'adaptació denominat Look Ahead el qual, sense modificar l'estàndard, permet utilitzar la informació de les grandàries dels segments de vídeo inclosa en els contenidors multimèdia per a evitar prendre decisions d'adaptació que desemboquin en una parada indesitjada en la reproducció de contingut multimèdia. Amb l'objectiu d'avaluar les possibles millores de l'algoritme d'adaptació presentat, es proposen tres models d'avaluació objectiva de la QoE. Els models proposats permeten predir de manera senzilla la QoE que tindrien els usuaris de manera objectiva, utilitzant paràmetres coneguts com el bitrate mitjà, el PSNR (Peak Signal-to-Noise Ratio) i el valor de VMAF (Video Multimethod Assessment Fusion). Tots ells aplicats a cada segment. Finalment, s'estudia el comportament de DASH en entorns Wi-Fi amb alta densitat d'usuaris. En aquest context es produeixen un nombre elevat de parades en la reproducció per una mala estimació de la taxa de transferència disponible deguda al patró ON/OFF de descàrrega de DASH i a la variabilitat de l'accés al mitjà de Wi-Fi. Per a pal·liar aquesta situació, es proposa un servei de coordinació basat en la tecnologia SAND (MPEG's Server and Network Assisted DASH) que proporciona una estimació de la taxa de transferència basada en la informació de l'estat dels players dels clients.
[EN] This thesis presents several proposals aimed at improving video transmission through the DASH (Dynamic Adaptive Streaming over HTTP) standard. This research work studies the DASH transmission protocol and its characteristics. At the same time, this work proposes the use of encoding with constant quality and variable bitrate as the most suitable video content encoding mode for on-demand content transmission through the DASH standard. Based on the proposal to use the constant quality encoding mode, the role played by adaptation algorithms in the user experience when consuming multimedia content becomes more important. In this sense, this thesis presents an adaptation algorithm called Look Ahead which, without modifying the standard, allows the use of the information on the sizes of the video segments included in the multimedia containers to avoid making adaptation decisions that lead to undesirable stalls during the playback of multimedia content. In order to evaluate the improvements of the presented adaptation algorithm, three models of objective QoE evaluation are proposed. These models allow to predict in a simple way the QoE that users would have in an objective way, using well-known parameters such as the average bitrate, the PSNR (Peak Signal-to-Noise Ratio) and the VMAF (Video Multimethod Assessment Fusion). All of them applied to each segment. Finally, the DASH behavior in Wi-Fi environments with high user density is analyzed. In this context, there could be a high number of stalls in the playback because of a bad estimation of the available transfer rate due to the ON/OFF pattern of DASH download and to the variability of the access to the Wi-Fi environment. To relieve this situation, a coordination service based on SAND (MPEG's Server and Network Assisted DASH) is proposed, which provides an estimation of the transfer rate based on the information of the state of the clients' players.
Belda Ortega, R. (2021). Mejora del streaming de vídeo en DASH con codificación de bitrate variable mediante el algoritmo Look Ahead y mecanismos de coordinación para la reproducción, y propuesta de nuevas métricas para la evaluación de la QoE [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/169467
TESIS

5G Fixed Wireless Access (FWA) is an enabling technology in intelligent systems (IS) that may provide Ultra-HD (UHD) video streaming services with high Quality of Experience (QoE) in a small business use case setting. However, UHD streaming over 5G FWA is difficult in terms of latency and dependability due to numerous network factors. Due to this there may be multiple video players competing for network resources when streaming a UHD video. To date there has been very little work of 5G ‘last mile access’ streaming over bottleneck FWA Local Area Networks (LANs) under congested network conditions. The bottleneck link is the 5G FWA gateway. In these networks viewers may get sub-optimal QoE. Adaptive bitrate (ABR) algorithms are used to select the near optimal bitrates during a streaming session. To obtain the QoE of viewers in 5G FWA bottleneck networks we study the performance of four DASH-based adaptive video streaming algorithms (MPC, BOLA, Oboe and Pensive). BOLA performs the best and Pensive the worst. However, BOLA’s overall performance is sub-optimal. This work supports the need for developing new ABR algorithms for the 5G FWA environment.