%0 Conference Paper %1 sch19ITC31 %A Schwarzmann, Susanna %A Breitbach, Paula %A Zinner, Thomas %A Rost, Matthias %B 31th International Teletraffic Congress (ITC 31) %C Budapest, Hungary %D 2019 %K itc itc31 %T Modeling Adaptive Video Streaming Using Discrete-Time Analysis %U https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc31/sch19ITC31.pdf?inline=true %X HTTP Adaptive Streaming (HAS) is the de-facto standard for video delivery over the Internet. Video clips are split into segments and the quality can dynamically be adapted by choosing between multiple quality levels per segment. Based on client-centric parameters like the video buffer state or the throughput, an adaptive bitrate (ABR) algorithm decides the quality of the segments. Besides the applied ABR algorithm, a multitude of parameters influence the Quality-of-Experience (QoE) like network and video characteristics, buffer thresholds, number of provided quality levels, and segment durations. This results in a highly complex interaction between the different system parameters. However, the interdependence of these parameters has not been explored in a holistic manner, yet. In this paper, a generic performance model for throughput-based and buffer based ABR algorithms is proposed. Using discrete-time analysis, the model allows to compute relevant HAS metrics such as video interruptions and play back quality. To highlight the practical applicability of the model, an extensive evaluation is presented, in which the probabilistic model's results are compared with actual measurements of simplistic buffer-based and rate-based ABR algorithms. The results indicate that the model is sufficiently expressive to model the behavior in various settings, while still remaining computationally tractable.

@inproceedings{sch19ITC31, abstract = {HTTP Adaptive Streaming (HAS) is the de-facto standard for video delivery over the Internet. Video clips are split into segments and the quality can dynamically be adapted by choosing between multiple quality levels per segment. Based on client-centric parameters like the video buffer state or the throughput, an adaptive bitrate (ABR) algorithm decides the quality of the segments. Besides the applied ABR algorithm, a multitude of parameters influence the Quality-of-Experience (QoE) like network and video characteristics, buffer thresholds, number of provided quality levels, and segment durations. This results in a highly complex interaction between the different system parameters. However, the interdependence of these parameters has not been explored in a holistic manner, yet. In this paper, a generic performance model for throughput-based and buffer based ABR algorithms is proposed. Using discrete-time analysis, the model allows to compute relevant HAS metrics such as video interruptions and play back quality. To highlight the practical applicability of the model, an extensive evaluation is presented, in which the probabilistic model's results are compared with actual measurements of simplistic buffer-based and rate-based ABR algorithms. The results indicate that the model is sufficiently expressive to model the behavior in various settings, while still remaining computationally tractable.}, added-at = {2020-04-29T15:29:04.000+0200}, address = {Budapest, Hungary}, author = {Schwarzmann, Susanna and Breitbach, Paula and Zinner, Thomas and Rost, Matthias}, biburl = {https://www.bibsonomy.org/bibtex/23410f8e7bb6e936d61a810f1d753ae62/itc}, booktitle = {31th International Teletraffic Congress (ITC 31)}, interhash = {33b56fc22f7a5309ba6df38dbacbcde6}, intrahash = {3410f8e7bb6e936d61a810f1d753ae62}, keywords = {itc itc31}, timestamp = {2020-04-30T18:18:45.000+0200}, title = {Modeling Adaptive Video Streaming Using Discrete-Time Analysis}, url = {https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc31/sch19ITC31.pdf?inline=true}, year = 2019 }