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A Bandwidth Estimation Scheme to Improve the QoE of HTTP Adaptive Streaming in the Multiple Client Environment

  • Kim, Sangwook (Department of Electronics and Communications Engineering, Kwangwoon University) ;
  • Chung, Kwangsue (Department of Electronics and Communications Engineering, Kwangwoon University)
  • Received : 2017.03.14
  • Accepted : 2017.09.15
  • Published : 2018.01.31

Abstract

HTTP adaptive streaming (HAS) is a promising technology for delivering video content over the Internet. HAS-based video streaming solutions rely on bandwidth estimation to select the appropriate video bitrate. Video streaming solutions that consider network conditions provide users with seamless video playback. However, when multiple clients compete for a common bottleneck link, conventional bandwidth estimation schemes that consider only one client overestimate the network bandwidth due to the ON-OFF traffic pattern. The bandwidth overestimation can cause Quality of Experience (QoE) degradation, such as unnecessary changes in video quality, and unfairness of video quality. In this paper, we propose a client-side bandwidth estimation scheme to obtain a better QoE of HAS in the multiple-client environment. The proposed scheme differentiates the client buffer status according to the buffer occupancy, and then estimates the available network bandwidth based on the buffer status and segment throughput. We evaluate the performance of HAS implemented in the ns-3 network simulator. Simulation results show that compared with the conventional schemes, the proposed scheme can enhance the QoE.

Keywords

References

  1. Cisco, "Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2014-2019," 2015.
  2. M. Seufert, S. Egger, M. Slanina, T. Zinner, T. Hobfeld, and P. Tran-Gia, "A Survey on Quality of Experience of HTTP Adaptive Streaming," IEEE Communications Survey and Tutorials, vol 17, no. 1, pp. 469-492, Mar. 2015. https://doi.org/10.1109/COMST.2014.2360940
  3. T. Mangla, N. Theera-Ampornpunt, M. Ammar, E. Zegura, and S. Bagchi, "Video Through a Crystal Ball: Effect of Bandwidth Prediction Quality on Adaptive Streaming in Mobile Environments," in Proc. of the ACM 8th International Workshop on Mobile Video, May 2016.
  4. Microsoft, Smooth Streaming, [Online]. Available: http://www.iis.net/downloads/smooth-streaming/
  5. Apple, HTTP Live Streaming, [Online]. Available: http://developer.apple.com/rescources/http-streaming/
  6. Adobe, HTTP Dynamic Streaming, [Online]. Available: http://www.adobe.com/products/httpdynamicstreaming/
  7. X. Yin, V. Sekar, and B. Sinopoli, "Toward a Principled Framework to Design Dynamic Adaptive Streaming Algorithm over HTTP," in Proc. of the ACM workshop on Hot Topics in Networks, pp. 1-9, Oct. 2014.
  8. T. Stockhammer, "Dynamic Adaptive Streaming over HTTP: Standards and Design Principles," in Proc. of the ACM Multimedia Systems, pp. 133-144, Feb. 2011.
  9. V. Jacobson, "Congestion avoidance and control," in Proc. of the ACM Conference on Symposium proceedings on Communications architectures and protocols, pp. 314-329, 1988.
  10. S. Akhshabi, L. Anantakrishnan, A. C. Begen, and C. Dovrolis, "What Happens When HTTP Adaptive Streaming Players Compete for Bandwidth?," in Proc. of the ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, pp.9-14, Jun. 2012.
  11. Z. Li, X. Zhu, J. Gahm, R. Pan, H. Hu, A. C. Began, and D. Oran, "Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale," IEEE Journal on Selected Areas in Communications, vol. 32, no. 4, pp. 719-733, Apr. 2014. https://doi.org/10.1109/JSAC.2014.140405
  12. A. Begen, T. Akgul, and M. Baugher, "Watching Video over the Web, Part 1: Streaming Protocols," IEEE Internet Computing, vol. 15, no. 2, pp. 54-63, Mar. 2011. https://doi.org/10.1109/MIC.2010.155
  13. T. Huang, N. Handigol, B. Heller, N. McKeown, and R. Johari, "Confused, Timid, and Unstable: Picking a Video Streaming Rate is Hard," in Proc. of the ACM Conference on Internet Measurement Conference, pp. 225-238, Nov. 2012.
  14. F. Dobrian, V. Sekar, A. Awan, I. Stoica, D. Joseph, A. Ganjam, J. Zhan, and H. Zhang, "Understanding the impact of video quality on user engagement," ACM SIGCOMM Computer Communication Review, vol. 41, no. 4, pp. 362-373, Aug. 2011. https://doi.org/10.1145/2043164.2018478
  15. Y. Liu, S. Dey, D. Gillies, F. Ulupinar, and M. Luby, "User Experience Modeling for DASH Video," in Proc. of the IEEE Packet Video Workshop, pp. 1-8, Dec. 2013.
  16. P. Ni, R. Eg, A. Eichhorn, C. Griwodz, and P. Halvorsen, "Flicker effects in adaptive video streaming to handheld devices," in Proc. of ACM International Conference on Multimedia, pp. 463-472, Nov. 2011.
  17. L. Zhou, Z. Yang, Y. Wen, H. Wang, and M. Guizani, "Resource Allocation with Incomplete Information for QoE-driven Multimedia Communications," IEEE Transactions on Wireless Communications, vol. 12, no. 8, pp. 3733-3745, Aug. 2013. https://doi.org/10.1109/TWC.2013.051413.120597
  18. C. Liu, I. Bouazizi, and M. Gabbouj, "Rate Adaptation for Adaptive HTTP Streaming," in Proc. of the ACM Multimedia Systems, pp. 169-174, Feb. 2011.
  19. T. Thang, Q. Ho, J. Kang, and A. Pham, "Adaptive Streaming of Audiovisual Content Using MPEG DASH," IEEE Transactions on Consumer Electronics, vol. 58, no. 1, pp. 78-83, Feb. 2012. https://doi.org/10.1109/TCE.2012.6170058
  20. E. Chan, R. Mok, X. Luo, and R. Chang, "Qdash: A Qoe-aware DASH System," in Proc. of the 3rd ACM Multimedia Systems Conference, pp. 11-22, Feb. 2012.
  21. S. Akhshabi, A. C. Begen, and C. Dovrolis, "An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP," in Proc. of ACM Conference on Multimedia System, pp. 157-168, Feb. 2011.
  22. R. Dubin, O. Hadar, and A. Dvir, "The effect of client buffer and MBR consideration on DASH adaptation logic," in Proc. of IEEE Wireless Communications and Networking Conference, pp. 2178-2183, Apr. 2013.
  23. W. Rahman and K. Chung, "Buffer-based adaptive bitrate algorithm for streaming over HTTP," KSII Transactions on Internet and Information Systems, vol. 9, no. 11, pp. 4585-4622, Nov. 2015. https://doi.org/10.3837/tiis.2015.11.019
  24. VideoLAN. 2013. Vlc sourece code. [Online]. Available: http://www.videolan.org/vlc/download-sources.html.
  25. R. Jain, D. Chiu, and W. Hawe, "A Quantitative Measure of Fairness and Discrimination for Resource Allocation in Shared Computer Systems," Digital Equip. Corp., Littleton, MA, DEC Rep., DEC-TR-301, Sep. 1984.
  26. The Network Simulator 3 [Online].
  27. A. Zambelli. Microsoft Corporation. "IIS smooth streaming technical overview". [Online].