%0 Conference Paper %1 7277441 %A Kleinrouweler, J.W. %A Cabrero, S. %A van der Mei, R. %A Cesar, P. %B Teletraffic Congress (ITC 27), 2015 27th International %D 2015 %K Adaptation_models Bandwidth Bit_rate HTTP_proxy_server Markov_processes Servers Stability_analysis Streaming_media bandwidth_allocation bandwidth_estimation channel_unfair_sharing hypermedia in-network_HAS_assistant itc itc27 network-assisted_HAS_technology network-assisted_HTTP_adaptive_streaming_player_bitrate_model network-assisted_HTTP_adaptive_streaming_player_stability_model telecommunication_channels telecommunication_network_planning video_delivery_network_planning video_quality video_servers video_stream_bitrate video_streaming %P 177-184 %R 10.1109/ITC.2015.28 %T Modeling Stability and Bitrate of Network-Assisted HTTP Adaptive Streaming Players %U https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc27/7277441.pdf?inline=true %X Viewers using HTTP Adaptive Streaming (HAS) without sufficient bandwidth undergo frequent quality switches that hinder their watching experience. This situation, known as instability, is produced when HAS players are unable to accurately estimate the available bandwidth. Moreover, when several players stream over a bottleneck link, their individual adaptation techniques may result in an unfair share of the channel. These are two detrimental issues in HAS technology, which is otherwise very attractive. To overcome them, a group of solutions are proposed in the literature that can be classified as network-assisted HAS. Solving stability and fairness only in the player is difficult, because a player has a limited view of the network. Using information from network devices can help players in making better adaptation decisions. The contribution of this paper is three-fold. First, we describe our implementation in the form of an HTTP proxy server, and show that both stability and fairness are strongly improved. Second, we present an analytical model that allows to compute the number of changes in video quality and the bitrate of a video stream. Third, we validate the accuracy of the model by comparing the model-based estimations for the number of changes in video quality and for the mean bitrate of a video stream, with results in a real implementation of our HAS assistant. The results show that the model-based results are highly accurate. As such, this model is useful in practice for planning video delivery networks that use in-network HAS assistants, and enables us to analyze the stability and the mean bitrate of HAS streams prior to real deployment.

@inproceedings{7277441, abstract = {Viewers using HTTP Adaptive Streaming (HAS) without sufficient bandwidth undergo frequent quality switches that hinder their watching experience. This situation, known as instability, is produced when HAS players are unable to accurately estimate the available bandwidth. Moreover, when several players stream over a bottleneck link, their individual adaptation techniques may result in an unfair share of the channel. These are two detrimental issues in HAS technology, which is otherwise very attractive. To overcome them, a group of solutions are proposed in the literature that can be classified as network-assisted HAS. Solving stability and fairness only in the player is difficult, because a player has a limited view of the network. Using information from network devices can help players in making better adaptation decisions. The contribution of this paper is three-fold. First, we describe our implementation in the form of an HTTP proxy server, and show that both stability and fairness are strongly improved. Second, we present an analytical model that allows to compute the number of changes in video quality and the bitrate of a video stream. Third, we validate the accuracy of the model by comparing the model-based estimations for the number of changes in video quality and for the mean bitrate of a video stream, with results in a real implementation of our HAS assistant. The results show that the model-based results are highly accurate. As such, this model is useful in practice for planning video delivery networks that use in-network HAS assistants, and enables us to analyze the stability and the mean bitrate of HAS streams prior to real deployment.}, added-at = {2016-02-17T10:46:22.000+0100}, author = {Kleinrouweler, J.W. and Cabrero, S. and van der Mei, R. and Cesar, P.}, biburl = {https://www.bibsonomy.org/bibtex/2a9412ad01595b340d7eb931c37781c4f/itc}, booktitle = {Teletraffic Congress (ITC 27), 2015 27th International}, doi = {10.1109/ITC.2015.28}, interhash = {e460433a78f627fe9597a36efe37375a}, intrahash = {a9412ad01595b340d7eb931c37781c4f}, keywords = {Adaptation_models Bandwidth Bit_rate HTTP_proxy_server Markov_processes Servers Stability_analysis Streaming_media bandwidth_allocation bandwidth_estimation channel_unfair_sharing hypermedia in-network_HAS_assistant itc itc27 network-assisted_HAS_technology network-assisted_HTTP_adaptive_streaming_player_bitrate_model network-assisted_HTTP_adaptive_streaming_player_stability_model telecommunication_channels telecommunication_network_planning video_delivery_network_planning video_quality video_servers video_stream_bitrate video_streaming}, month = {Sept}, pages = {177-184}, timestamp = {2020-04-30T18:18:14.000+0200}, title = {Modeling Stability and Bitrate of Network-Assisted HTTP Adaptive Streaming Players}, url = {https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc27/7277441.pdf?inline=true}, year = 2015 }