QoE Optimization Based on Quality-delay Trade-off Model for Adaptive Streaming with Multiple VBR Videos
Main Article Content
Abstract
HTTP adaptive streaming (HAS) as a part of multi-bitrate streaming, has attracted significant attention over the past few years. Although offering many advantages such as easy deployment and effective cost, HAS faces some challenges in providing users with high video quality. In managed networks (i.e., IPTV), the purely client-driven approaches of current HAS cause competing behavior, excessive quality oscillations, which negatively affect user experience. Some recent studies have proposed network-based solutions to overcome these problems; however, they just target at constant bitrate (CBR) videos. In this paper, we propose a quality optimization solution for variable bitrate (VBR) video streaming which allows components inside the network to select an appropriate version for each HAS client. The experiments in real-time conditions show that our method can provide each HAS client with the best possible quality while meeting the constraints of overall bandwidth and delay.
Keywords
Adaptive Streaming, QoE, Optimization, Variable bitrate (VBR)
Article Details
References
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[3] S. Akhshabi, L. Anantakrishnan, A. C. Begen, and C. Dovrolis, What happens when HTTP adaptive streaming players compete for bandwidth?, in Proc. Int. Workshop Netw. Oper. Syst. Support Digit. Audio Video, pp. 9–14, 2012.
[4] C. Ben Ameur, E. Mory and B. Cousin, Evaluation of Gateway-Based Shaping Methods for HTTP Adaptive Streaming, in Proc. IEEE ICC Workshop Quality ExperienceBased Manage. Future Internet Applicat. and Services, London, 2015.
[5] A. Essaili et al., QoE-based Traffic and Resource Management for Adaptive HTTP Video Delivery in LTE, IEEE Trans. Circuits Syst. Video Technol., vol. 25, no. 6, pp. 988-1001, Nov. 2014.
[6] P. Georgopoulos et al., Towards network-wide QoE fairness using openflow-assisted adaptive video streaming, in Proc. ACM SIGCOMM Workshop Future HumanCentric Multimedia Netw., pp. 15-20, 2013.
[7] N. Bouten et al., In-network .QoE optimization through in-network quality adaptation for HTTP adaptive streaming, in Proc. 8th Int. CNSM, Las Vegas, USA, pp. 336-342, 2012.
[8] W. Pu, Z. Zou, and C. W. Chen, Video adaptation proxy for wireless dynamic adaptive streaming over HTTP, in Proc. Int. Packet Video Workshop, pp. 65–70, 2012.
[9] Duc V. Nguyen, Nam Pham Ngoc, Dung M. Nguyen, Anh T. Pham, T. C. Thang, Adaptive home surveillance system using HTTP streaming, in Proc. ICAST-UMEDIA, pp.579-584, Nov. 2013.
[10] G. D. Forney, The Viterbi algorithm, in Proc. IEEE, vol. 61, no. 3, pp. 268-78, Mar. 1973.
[11] K. Miller, E. Quacchio, G. Gennari, and A. Wolisz, Adaptation algorithm for adaptive streaming over http, in Packet Video Workshop (PV), 2012 19th International, May 2012, pp. 173–178.
[12] T.-Y. Huang, R. Johari, N. McKeown, M. Trmnell, and M. Watson, ‘A buffer-based approach to rate adaptation: Evidence from a large video streaming service, in Proc. of the ACM SIGCOMM, 2014, pp. 187-198.
[13] C. Muller, S. Lederer, and C. Timmerer, An evaluation of dynamic adaptive streaming over http in vehicular environments, in Proceedings of the 4th Workshop on Mobile Video, ser. MoVid ’12. New York, NY, USA: ACM, 2012, pp. 37–42.
[14] T. C. Thang, H. T. Le, A. T. Pham, Y. M. Ro, An Evaluation of Bitrate Adaptation Methods for HTTP Live Streaming, IEEE Journal on Selected Areas in Communications, vol. 32, no. 4, pp. 693-705, April 2014.
[15] S. Garcia, J. Cabrera, N. Garcia, Quality-Control Algorithm for Adaptive Streaming Services Over Wireless Channels, IEEE Journal of Selected Topics in Signal Processing, vol. 9, no. 1, pp. 50-59, Feb. 2015.
[16] T. C. Thang, H. T. Le, H. X. Nguyen, A. T. Pham, J. W. Kang, Y. M. Ro, Adaptive Video Streaming Over HTTP with Dynamic Resource Estimation, IEEE Trans. on Consumer Electronics, vol. 58, no. 1, pp. 78-85, Feb. 2012.
[17] T. Stockhammer, Dynamic Adaptive Streaming over HTTP–– Standards and Design Principles, in Proc. ACM MMSys, California, pp. 133-143, Feb. 2011.
[18] B. J. Villa, P. E. Heegaard, and A. Imstefjord, Improving fairness for adaptive http video streaming, in Information and Communication Technologies, Robert Szabo and Attila Vudaes (Eds.), Lecture Notes in Computer Science, vol. 7479. Springer, Berlin, , 2012, pp. 183-193.
[19] S. Akhshabi, L. Anantakrishnan, C. Dovrolis, and A. C. Begen, Server-based traffic shaping for stabilizing oscillating adaptive streaming players, in Proc. 23rd ACM Int. Workshop NOSS DAV, Oslo, Norway, 2013, pp. 19-24.
[20] Zhang D, He H, Li W, Bitrate allocation among multiple video streams to maximize profit in content delivery networks, Personal and Ubiqitous Computing, vol. 20, no. 3, p. 385-396, 2016.
[21] S. Petrangeli, J. Famaey, M. Claeys, S. Latré, and F. De Turck, Qoe-driven Rate Adaptation Heuristic for Fair Adaptive Video Streaming, ACM Trans. Multimedia Comput. Commun. Appl., Oct 2015, 12 (2):28:1–28:24, doi:10.1145/2818361. URL http://doi.acm.org/10.1145/2818361.
[22] Y. Huang, and S. Mao, Downlink power control for multi-user VBR video streaming in cellular networks, IEEE Trans. Multimedia, vol. 15, no. 8, pp. 2137-2148, Dec. 2013.
[23] Duc V. Nguyen, Nam Pham Ngoc, Dung M. Nguyen, Anh T. Pham, T. C. Thang, Quality-Delay Tradeoff Optimization in Multi-Bitrate Adaptive Streaming, in Proc. IEEE ICCE, Las Vegas, US, Jan. 2013.
[24] Garey, Michael R., and Johnson, David S., Computers and Intractability: A Guide to the Theory of NP-Completeness, W.H. Freeman and Company, New York, 1979.
[25] Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein, Introduction to Algorithms,3rd Edition, MIT Press and McGraw-Hill., 2009.
[26] Geert Van Der Auwera, Prasanth T. David, Martin Reisslein, Traffic and Quality Characterization of Single-Layer Video Streams Encoded with H.264/MPEG-4 Advanced Video Coding Standard and Scalable Video Coding Extension, IEEE Trans. Broadcast., vol. 54, no. 3, pp. 698-718, Sep. 2008.