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Design and Analysis of QoE-Aware Quality Adaptation for DASH: A Spectrum-Based Approach

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Published:14 July 2017Publication History
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Abstract

The dynamics of the application-layer-based control loop of dynamic adaptive streaming over HTTP (DASH) make video bitrate selection for DASH a difficult problem. In this work, we provide a DASH quality adaptation algorithm, named SQUAD, that is specifically tailored to provide a high quality of experience (QoE). We review and provide new insights into the challenges for DASH rate estimation. We found that in addition to the ON-OFF behavior of DASH clients, there exists a discrepancy in the timescales that form the basis of the rate estimates across (i) different video segments and (ii) the rate control loops of DASH and Transmission Control Protocol (TCP). With these observations in mind, we design SQUAD aiming to maximize the average quality bitrate while minimizing the quality variations. We test our implementation of SQUAD together with a number of different quality adaptation algorithms under various conditions in the Global Environment for Networking Innovation testbed, as well as, in a series of measurements over the public Internet. Through a measurement study, we show that by sacrificing little to nothing in average quality bitrate, SQUAD can provide significantlygt; better QoE in terms of quality switching and magnitude. In addition, we show that retransmission of higher-quality segments that were originally received in low-quality is feasible and improves the QoE.

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References

  1. Adobe. 2016. Adobe HTTP Dynamic Streaming. Retrieved September 23, 2016 from http://www.adobe.com/products/hds-dynamic-streaming.html.Google ScholarGoogle Scholar
  2. S. Akhshabi, L. Anantakrishnan, A. C. Begen, and C. Dovrolis. 2012. What happens when HTTP adaptive streaming players compete for bandwidth?. In Proceedings of the ACM SIGMM Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV’16). 9--14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. S. Akhshabi, A. C. Begen, and C. Dovrolis. 2011. An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP. In Proceedings of the ACM Multimedia Systems Conference (MMSys’11). 157--168. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Apple. 2016. Apple HTTP Live Streaming. Retrieved September 23, 2016 from https://developer.apple.com/resources/http-streaming/.Google ScholarGoogle Scholar
  5. A. Beben, P. Wiśniewski, J. Mongay Batalla, and P. Krawiec. 2016. ABMA+: Lightweight and efficient algorithm for HTTP adaptive streaming. In Proceedings of the ACM Multimedia Systems Conference (MMSys’16). 2:1--2:11. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. M. Berman, J. S. Chase, L. Landweber, A. Nakao, M. Ott, D. Raychaudhuri, R. Ricci, and I. Seskar. 2014. GENI: A federated testbed for innovative network experiments. Comput. Netw. 61, 0 (2014), 5--23. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. M. Berman, P. Demeester, J. W. Lee, K. Nagaraja, M. Zink, D. Colle, D. K. Krishnappa, D. Raychaudhuri, H. Schulzrinne, I. Seskar, and S. Sharma. 2015. Future Internets escape the simulator. Commun. ACM 58, 6 (May 2015), 78--89. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. L. De Cicco, S. Mascolo, and V. Palmisano. 2011. Feedback control for adaptive live video streaming. In Proceedings of the ACM Multimedia Systems Conference (MMSys’11). 145--156. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. F. Fund, C. Wang, Y. Liu, T. Korakis, M. Zink, and S. S. Panwar. 2013. Performance of DASH and WebRTC video services for mobile users. In Proceedings of the IEEE Packet Video Workshop (PV). 1--8. Google ScholarGoogle ScholarCross RefCross Ref
  10. G. Giambene. 2005. Queuing Theory and Telecommunications: Networks and Applications. Springer, Berlin.Google ScholarGoogle Scholar
  11. T. Huang, N. Handigol, B. Heller, N. McKeown, and R. Johari. 2012. Confused, timid, and unstable: Picking a video streaming rate is hard. In Proceedings of the Internet Measurement Conference (IMC’12). 225--238. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. T. Huang, R. Johari, N. McKeown, M. Trunnell, and M. Watson. 2014. A buffer-based approach to rate adaptation: evidence from a large video streaming service. In Proceedings of the ACM International Conference of the Special Interest Group on Data Communications (SIGCOMM’14). 187--198. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Bitmovin Inc. 2016. Optimal Segment Length for Adaptive Streaming Formats like MPEG-DASH 8 HLS. Retrieved September 23, 2016 from http://www.dash-player.com/blog/2015/04/using-the-optimal-segment-length-for-adaptive-streaming-formats-like-mpeg-dash-hls/.Google ScholarGoogle Scholar
  14. M. Jain and C. Dovrolis. 2003. End-to-end available bandwidth: Measurement methodology, dynamics, and relation with TCP throughput. IEEE/ACM Trans. Netw. 11, 4 (Aug. 2003), 537--549. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Raj Jain. 1991. The Art of Computer Systems Performance Analysis—Techniques for Experimental Design, Measurement, Simulation, and Modeling. Wiley. I--XXVII, 1--685 pages.Google ScholarGoogle Scholar
  16. P. Juluri, V. Tamarapalli, and D. Medhi. 2015. SARA: Segment-aware rate adaptation algorithm for dynamic adaptive streaming over HTTP. In Proceedings of the IEEE ICC Quality of Experience-based Management for Future Internet Applications and Services Workshop (QoE-FI’15). 1765--1770. Google ScholarGoogle ScholarCross RefCross Ref
  17. S. Shunmuga Krishnan and Ramesh K. Sitaraman. 2012. Video stream quality impacts viewer behavior: Inferring causality using quasi-experimental designs. In Proceedings of the Internet Measurement Conference (IMC’12). 211--224. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. S. Lederer, C. Müller, and C. Timmerer. 2012. Dynamic adaptive streaming over HTTP dataset. In Proceedings of the ACM Multimedia Systems Conference (MMSys’12). 89--94. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Z. Li, A. C. Begen, J. Gahm, Y. Shan, B. Osler, and D. Oran. 2014a. Streaming video over HTTP with consistent quality. In Proceedings of the ACM Multimedia Systems Conference (MMSys’14). 248--258. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Z. Li, X. Zhu, J. Gahm, R. Pan, H. Hu, A. C. Begen, and D. Oran. 2014b. Probe and adapt: Rate adaptation for HTTP video streaming at scale. IEEE J. Select. Areas Commun. 32, 4 (April 2014), 719--733. Google ScholarGoogle ScholarCross RefCross Ref
  21. J. Liebeherr, M. Fidler, and S. Valaee. 2010. A system-theoretic approach to bandwidth estimation. IEEE/ACM Trans. Netw. 18, 4 (2010), 1040--1053. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Microsoft. 2016. Microsoft Smooth Streaming. Retrieved September 23, 2016 from http://www.iis.net/downloads/microsoft/smooth-streaming.Google ScholarGoogle Scholar
  23. C. Müller and C. Timmerer. 2011. A VLC media player plugin enabling dynamic adaptive streaming over HTTP. In Proceedings of the ACM Multimedia Systems Conference (MMSys’11). 723--726. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. O. Oyman and S. Singh. 2012. Quality of experience for HTTP adaptive streaming services. IEEE Commun. Mag. 50, 4 (April 2012), 20--27. Google ScholarGoogle ScholarCross RefCross Ref
  25. A. Rao, A. Legout, Y. Lim, D. Towsley, C. Barakat, and W. Dabbous. 2011. Network characteristics of video streaming traffic. In Proceedings of the Conference on Emerging Networking Experiments and Technologies (CoNEXT’11). Article 25, 12 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. I. Sodagar. 2011. The MPEG-DASH standard for multimedia streaming over the Internet. IEEE MultiMedia 18, 4 (April 2011), 62--67. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. K. Spiteri, R. Urgaonkar, and R. K. Sitaraman. 2016. BOLA: Near-optimal bitrate adaptation for online videos. In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM’16). 1--9. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. G. Tian and Y. Liu. 2012. Towards agile and smooth video adaptation in dynamic HTTP streaming. In Proceedings of the Conference on Emerging Networking Experiments and Technologies (CoNEXT’12). 109--120. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Sandvine Incorporated ULC. 2016. Global Internet Phenomena Report 2016. Retrieved January 3, 2017 from https://www.sandvine.com/downloads/general/global-internet-phenomena/2016/global-internet-phenomena-report-latin-america-and-north-america.pdf.Google ScholarGoogle Scholar
  30. B. J. Villa and P. E. Heegaard. 2013. Group based traffic shaping for adaptive HTTP video streaming by segment duration control. In Proceedings of the IEEE International Conference on Advanced Information Networking and Applications (AINA’13). 830--837. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Ashish Vulimiri, Philip Brighten Godfrey, Radhika Mittal, Justine Sherry, Sylvia Ratnasamy, and Scott Shenker. 2013. Low latency via redundancy. In Proceedings of the Conference on Emerging Networking Experiments and Technologies (CoNEXT’13). 283--294. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. C. Wang, A. Rizk, and M. Zink. 2016. SQUAD: A spectrum-based quality adaptation for dynamic adaptive streaming over HTTP. In Proceedings of the ACM Multimedia Systems Conference (MMSys’16). 1:1--1:12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. B. White, J. Lepreau, L. Stoller, R. Ricci, S. Guruprasad, M. Newbold, M. Hibler, C. Barb, and A. Joglekar. 2002. An integrated experimental environment for distributed systems and networks. In Proceedings of the Organization for Social Development Initiatives (OSDI’02). 255--270. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. J. Whiteaker, F. Schneider, and R. Teixeira. 2011. Explaining packet delays under virtualization. SIGCOMM Comput. Commun. Rev. 41, 1 (Jan. 2011), 38--44. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. S. Xiang, L. Cai, and J. Pan. 2012. Adaptive scalable video streaming in wireless networks. In Proceedings of the ACM Multimedia Systems Conference (MMSys’12). 167--172. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. X. Yin, A. Jindal, V. Sekar, and B. Sinopoli. 2015. A control-theoretic approach for dynamic adaptive video streaming over HTTP. In Proceedings of the ACM International Conference of the Special Interest Group on Data Communications (SIGCOMM’15). 325--338. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. M. Zink, J. Schmitt, and R. Steinmetz. 2005. Layer-encoded video in scalable adaptive streaming. IEEE Trans. Multimedia 7, 1 (Feb. 2005), 75--84. Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 13, Issue 3s
      Special Section on Deep Learning for Mobile Multimedia and Special Section on Best Papers from ACM MMSys/NOSSDAV 2016
      August 2017
      258 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/3119899
      Issue’s Table of Contents

      Copyright © 2017 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 14 July 2017
      • Accepted: 1 March 2017
      • Revised: 1 January 2017
      • Received: 1 November 2016
      Published in tomm Volume 13, Issue 3s

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