Want to play DASH?: a game theoretic approach for adaptive streaming over HTTP

In streaming media, it is imperative to deliver a good viewer experience to preserve customer loyalty. Prior research has shown that this is rather difficult when shared Internet resources struggle to meet the demand from streaming clients that are largely designed to behave in their own self-interest. To date, several schemes for adaptive streaming have been proposed to address this challenge with varying success. In this paper, we take a different approach and develop a game theoretic approach. We present a practical implementation integrated in the dash.js reference player and provide substantial comparisons against the state-of-the-art methods using trace-driven and real-world experiments. Our approach outperforms its competitors in the average viewer experience by 38.5% and in video stability by 62%.

[1]  Wei Tsang Ooi,et al.  QUETRA: A Queuing Theory Approach to DASH Rate Adaptation , 2017, ACM Multimedia.

[2]  Phuoc Tran-Gia,et al.  A Survey on Quality of Experience of HTTP Adaptive Streaming , 2015, IEEE Communications Surveys & Tutorials.

[3]  Te-Yuan Huang,et al.  A buffer-based approach to rate adaptation: evidence from a large video streaming service , 2015, SIGCOMM 2015.

[4]  Stephen P. Boyd,et al.  Fast Model Predictive Control Using Online Optimization , 2010, IEEE Transactions on Control Systems Technology.

[5]  Luca De Cicco,et al.  ELASTIC: A Client-Side Controller for Dynamic Adaptive Streaming over HTTP (DASH) , 2013, 2013 20th International Packet Video Workshop.

[6]  M. Dufwenberg Game theory. , 2011, Wiley interdisciplinary reviews. Cognitive science.

[7]  Ali C. Begen,et al.  Enhancing MPEG DASH performance via server and network assistance , 2017 .

[8]  Zhu Han,et al.  Game Theory in Wireless and Communication Networks: Theory, Models, and Applications , 2011 .

[9]  Yong Liu,et al.  Towards agile and smooth video adaptation in dynamic HTTP streaming , 2012, CoNEXT '12.

[10]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[11]  Vyas Sekar,et al.  Understanding the impact of video quality on user engagement , 2011, SIGCOMM.

[12]  Xin Jin,et al.  Can Accurate Predictions Improve Video Streaming in Cellular Networks? , 2015, HotMobile.

[13]  Daniel Pérez Palomar,et al.  A tutorial on decomposition methods for network utility maximization , 2006, IEEE Journal on Selected Areas in Communications.

[14]  Ali C. Begen,et al.  SDNHAS: An SDN-Enabled Architecture to Optimize QoE in HTTP Adaptive Streaming , 2017, IEEE Transactions on Multimedia.

[15]  I. Stoica,et al.  A case for a coordinated internet video control plane , 2012, CCRV.

[16]  Adam Wolisz,et al.  QoE-Based Low-Delay Live Streaming Using Throughput Predictions , 2016, ACM Trans. Multim. Comput. Commun. Appl..

[17]  Xi Liu,et al.  C3: Internet-Scale Control Plane for Video Quality Optimization , 2015, NSDI.

[18]  Hongzi Mao,et al.  Neural Adaptive Video Streaming with Pensieve , 2017, SIGCOMM.

[19]  Kai Zeng,et al.  Display device-adapted video quality-of-experience assessment , 2015, Electronic Imaging.

[20]  Richard S. Sutton,et al.  Introduction to Reinforcement Learning , 1998 .

[21]  Reza Olfati-Saber,et al.  Consensus and Cooperation in Networked Multi-Agent Systems , 2007, Proceedings of the IEEE.

[22]  Carsten Griwodz,et al.  Commute path bandwidth traces from 3G networks: analysis and applications , 2013, MMSys.

[23]  Nick McKeown,et al.  Confused, timid, and unstable: picking a video streaming rate is hard , 2012, Internet Measurement Conference.

[24]  Ali C. Begen,et al.  SDNDASH: Improving QoE of HTTP Adaptive Streaming Using Software Defined Networking , 2016, ACM Multimedia.

[25]  Kagan Tumer,et al.  Optimal Payoff Functions for Members of Collectives , 2001, Adv. Complex Syst..

[26]  Ali C. Begen,et al.  An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP , 2011, MMSys.

[27]  Vyas Sekar,et al.  Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE , 2012, CoNEXT '12.

[28]  Bruno Sinopoli,et al.  A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP , 2015, Comput. Commun. Rev..

[29]  Ali C. Begen,et al.  QoE-Aware Bandwidth Broker for HTTP Adaptive Streaming Flows in an SDN-Enabled HFC Network , 2018, IEEE Transactions on Broadcasting.

[30]  Shijie Sun,et al.  Pytheas: Enabling Data-Driven Quality of Experience Optimization Using Group-Based Exploration-Exploitation , 2017, NSDI.

[31]  Ramesh K. Sitaraman,et al.  BOLA: Near-Optimal Bitrate Adaptation for Online Videos , 2016, IEEE/ACM Transactions on Networking.

[32]  Deep Medhi,et al.  SARA: Segment aware rate adaptation algorithm for dynamic adaptive streaming over HTTP , 2015, 2015 IEEE International Conference on Communication Workshop (ICCW).

[33]  Yi Sun,et al.  CS2P: Improving Video Bitrate Selection and Adaptation with Data-Driven Throughput Prediction , 2016, SIGCOMM.

[34]  Ali C. Begen,et al.  What happens when HTTP adaptive streaming players compete for bandwidth? , 2012, NOSSDAV '12.

[35]  Gregory W. Cermak,et al.  The Relationship Among Video Quality, Screen Resolution, and Bit Rate , 2011, IEEE Transactions on Broadcasting.

[36]  Xiapu Luo,et al.  QDASH: a QoE-aware DASH system , 2012, MMSys '12.

[37]  Christian Timmerer,et al.  Dynamic adaptive streaming over HTTP dataset , 2012, MMSys '12.

[38]  Thomas Stockhammer,et al.  Dynamic adaptive streaming over HTTP --: standards and design principles , 2011, MMSys.

[39]  Zhengfang Duanmu,et al.  A Quality-of-Experience Index for Streaming Video , 2017, IEEE Journal of Selected Topics in Signal Processing.

[40]  Ali C. Begen,et al.  Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale , 2013, IEEE Journal on Selected Areas in Communications.