Cooperative Bargaining Game-Based Multiuser Bandwidth Allocation for Dynamic Adaptive Streaming Over HTTP

Dynamic adaptive streaming over HTTP (DASH) has emerged as an efficient technology for video streaming. For a DASH system, a most common case is that a limited server bandwidth is competed by multiusers. In order to improve user quality of experience (QoE) and guarantee fairness, we propose to use the game theory in a proxy server to allocate the bandwidth collaboratively for multiusers. By taking user buffer length, received video bit rates, video qualities, etc., into account, the bandwidth allocation problem is formulated as a cooperative bargaining problem and the Nash bargaining solution (NBS) is obtained by convex optimization. The requested bit rate of users will be rewritten as the proxy calculated bit rate (i.e., NBS) when the user requested bit rate is larger. Experimental results demonstrate that user QoE and fairness can be improved significantly, i.e., the delay frequency and duration are smaller, and the received video qualities are higher and more stable, when comparing the proposed method with existing methods.

[1]  Mihaela van der Schaar,et al.  Bargaining Strategies for Networked Multimedia Resource Management , 2007, IEEE Transactions on Signal Processing.

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

[3]  Xirong Que,et al.  User K 1-DASH Get Request M-QAM modulation CSI High bandwidth Low delay Wireless channel Buffer Demodulation , 2014 .

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

[5]  Dirk Staehle,et al.  QoE-Based Traffic and Resource Management for Adaptive HTTP Video Delivery in LTE , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[6]  Bing Zhou,et al.  Bandwidth estimation and rate adaptation in HTTP streaming , 2012, 2012 International Conference on Computing, Networking and Communications (ICNC).

[7]  Velio Tralli,et al.  Improving QoE and Fairness in HTTP Adaptive Streaming Over LTE Network , 2016, IEEE Transactions on Circuits and Systems for Video Technology.

[8]  Liam Murphy,et al.  User perception of adapting video quality , 2006, Int. J. Hum. Comput. Stud..

[9]  J. Nash THE BARGAINING PROBLEM , 1950, Classics in Game Theory.

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

[11]  Mahbub Hassan,et al.  Optimizing HTTP-Based Adaptive Streaming in Vehicular Environment Using Markov Decision Process , 2015, IEEE Transactions on Multimedia.

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

[13]  Long Xu,et al.  Generalized Nash Bargaining Solution to Rate Control Optimization for Spatial Scalable Video Coding , 2014, IEEE Transactions on Image Processing.

[14]  Chia-Wen Lin,et al.  mDASH: A Markov Decision-Based Rate Adaptation Approach for Dynamic HTTP Streaming , 2016, IEEE Transactions on Multimedia.

[15]  Bruno Sinopoli,et al.  Toward a Principled Framework to Design Dynamic Adaptive Streaming Algorithms over HTTP , 2014, HotNets.

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

[17]  Chia-Wen Lin,et al.  A Control-Theoretic Approach to Rate Adaption for DASH Over Multiple Content Distribution Servers , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[18]  Salman Khan,et al.  A Link Adaptation Scheme for Efficient Transmission of H.264 Scalable Video Over Multirate WLANs , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

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

[20]  Moncef Gabbouj,et al.  Rate adaptation for adaptive HTTP streaming , 2011, MMSys.

[21]  Cheng Jin,et al.  FAST TCP: Motivation, Architecture, Algorithms, Performance , 2006, IEEE/ACM Transactions on Networking.

[22]  Jitendra K. Tugnait,et al.  QoE-Driven Resource Allocation for DASH over OFDMA Networks , 2016, 2016 IEEE Global Communications Conference (GLOBECOM).

[23]  Yicong Zhou,et al.  QoE Evaluation of Multimedia Services Based on Audiovisual Quality and User Interest , 2016, IEEE Transactions on Multimedia.

[24]  Raj Jain,et al.  A Quantitative Measure Of Fairness And Discrimination For Resource Allocation In Shared Computer Systems , 1998, ArXiv.

[25]  Nick McKeown,et al.  Downton abbey without the hiccups: buffer-based rate adaptation for HTTP video streaming , 2013, FhMN@SIGCOMM.

[26]  Holger Karl,et al.  Cross-layer scheduling for multi-quality video streaming in cellular wireless networks , 2013, 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC).

[27]  Yonggang Wen,et al.  QoE-Driven Cache Management for HTTP Adaptive Bit Rate Streaming Over Wireless Networks , 2012, IEEE Transactions on Multimedia.

[28]  Rajiv Soundararajan,et al.  Study of Subjective and Objective Quality Assessment of Video , 2010, IEEE Transactions on Image Processing.