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QoE for Mobile Clients with Segment-aware Rate Adaptation Algorithm (SARA) for DASH Video Streaming

Published:05 June 2019Publication History
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Abstract

Dynamic adaptive streaming over HTTP (DASH) is widely used for video streaming on mobile devices. Ensuring a good quality of experience (QoE) for mobile video streaming is essential, as it severely impacts both the network and content providers’ revenue. Thus, a good rate adaptation algorithm at the client end that provides high QoE is critically important. Recently, a segment size-aware rate adaptation (SARA) algorithm was proposed for DASH clients. However, its performance on mobile clients has not been investigated so far. The main contributions of this article are twofold: (1) We discuss SARA’s implementation for mobile clients to improve the QoE in mobile video streaming, one that accurately predicts the download time for the next segment and makes an informed bitrate selection, and (2) we developed a new parametric QoE model to compute a cumulative score that helps in fair comparison of different adaptation algorithms. Based on our subjective and objective evaluation, we observed that SARA for mobile clients outperforms others by 17% on average, in terms of the Mean Opinion Score, while achieving, on average, a 76% improvement in terms of the interruption ratio. The score obtained from our new parametric QoE model also demonstrates that the SARA algorithm for mobile clients gives a better QoE among all the algorithms.

References

  1. {n. d.}. DASH Adaptation Code and Results. Retrieved from https://sourceforge.net/projects/dash-adaptation-codGoogle ScholarGoogle Scholar
  2. {n. d.}. Dash.js Reference Client Implementation. Retrieved from https://github.com/Dash-Industry-Forum/dash.js.Google ScholarGoogle Scholar
  3. {n. d.}. Wondershaper—Traffic Shaping Script. Retrieved from https://packages.debian.org/unstable/net/wondershaper.Google ScholarGoogle Scholar
  4. {n. d.}. YouTube Recommended Resolution 8 Aspect Ratios. Retrieved from https://support.google.com/youtube/answer/6375112?hl=en.Google ScholarGoogle Scholar
  5. 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’12). ACM, 9--14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. S. Akhshabi, L. Anantakrishnan, C. Dovrolis, and A. C. Begen. 2013. Server-based traffic shaping for stabilizing oscillating adaptive streaming players. In Proceeding of the ACM SIGMM Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV’13). ACM, 19--24. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. 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 2nd ACM Multimedia Systems Conference (MMSys’11). 157--168. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. A. Bentaleb, A. C. Begen, and R. Zimmermann. 2016. SDNDASH: Improving QoE of HTTP adaptive streaming using software defined networking. In Proceedings of the 24th ACM International Conference on Multimedia. 1296--1305. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. A. Bentaleb, B. Taani, A. C. Begen, C. Timmerer, and R. Zimmermann. 2019. A survey on bitrate adaptation schemes for streaming media over HTTP. IEEE Commun. Surv. Tutor. 21, 1 (2019), 562--585.Google ScholarGoogle ScholarCross RefCross Ref
  10. N. Bouten, R. Schmidt, J. Famaey, S. Latré, A. Pras, and F. De Turck. 2015. QoE-driven in-network optimization for adaptive video streaming based on packet sampling measurements. Comput. Netw. 81 (Apr. 2015), 96--115. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. J. Bruneau-Queyreix, M. Lacaud, D. Negru, J. M. Batalla, and E. Borcoci. 2017. MS-Stream: A multiple-source adaptive streaming solution enhancing consumer’s perceived quality. In Proceedings of the IEEE Consumer Communications 8 Networking Conference (CCNC’17). 427--434.Google ScholarGoogle Scholar
  12. ITU-T Recommendation BT.500-13. 2012. Methodology for the Subjective Assessment of the Quality for Television Pictures. Retrieved from https://www.itu.int/rec/R-REC-BT.500-13-201201-I.Google ScholarGoogle Scholar
  13. Y. Chen, B. Zhang, Y. Liu, and W. Zhu. 2013. Measurement and modeling of video watching time in a large-scale internet video-on-demand system. IEEE Trans. Multimed. 15, 8 (Dec. 2013), 2087--2098. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Cisco Systems, Inc. 2017. Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2016--2021. Cisco White Paper. Retrieved from https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/mobile-white-paper-c11-520862.html.Google ScholarGoogle Scholar
  15. G. Cofano, L. De Cicco, T. Zinner, A. Nguyen-Ngoc, P. Tran-Gia, and S. Mascolo. 2017. Design and performance evaluation of network-assisted control strategies for HTTP adaptive streaming. ACM Trans. Multimedia Comput. Commun. Appl. 13, 3s, Article 42 (Jun. 2017), 24 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. COM 12-LS 62-E, TD 109rev2 (PLEN/12). 2007. Definition of Quality of Experience. ITU-T Study Group 12, Switzerland.Google ScholarGoogle Scholar
  17. P. Coverdale, S. Moller, A. Raake, and A. Takahashi. 2011. Multimedia quality assessment standards in ITU-T SG12. IEEE Sign. Process. Mag. 28, 6 (Nov. 2011), 91--97.Google ScholarGoogle ScholarCross RefCross Ref
  18. L. De Cicco, S. Mascolo, and V. Palmisano. 2011. Feedback control for adaptive live video streaming. In Proceedings of the 2nd ACM Multimedia Systems Conference (MMSys’11). 145--156. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Florin Dobrian, Vyas Sekar, Asad Awan, Ion Stoica, Dilip Joseph, Aditya Ganjam, Jibin Zhan, and Hui Zhang. 2011. Understanding the impact of video quality on user engagement. In Proceedings of ACM Special Interest Group on Data Communication (SIGCOMM’11). 362--373. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. J. Heidemann, K. Obraczka, and J. Touch. 1997. Modeling the performance of HTTP over several transport protocols. IEEE/ACM Trans. Netw. 5, 5 (Oct. 1997), 616--630. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. T.-Y. 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
  22. T.-Y. Huang, R. Johari, and N. McKeown. 2013. Downton abbey without the hiccups: Buffer-based rate adaptation for HTTP video streaming. In Proceedings of the ACM SIGCOMM Workshop on Future Human-centric Multimedia Networking (FhMN’13). 9--14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Te-Yuan Huang, Ramesh Johari, Nick McKeown, Matthew Trunnell, and Mark Watson. 2014. A buffer-based approach to rate adaptation: Evidence from a large video streaming service. In Proceedings of ACM Special Interest Group on Data Communication (SIGCOMM’14). 187--198. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. ITU-T Recommendation G.107. 2005. The E-model: A Computational Model for Use in Transmission Planning. Retreived from https://www.itu.int/rec/T-REC-G.107-201506-I/en.Google ScholarGoogle Scholar
  25. J. Jiang, V. Sekar, and H. Zhang. 2014. Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE. IEEE/ACM Trans. Netw. 22, 1 (Feb. 2014), 326--340. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. 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 International Conference on Communication Workshop. 1765--1770.Google ScholarGoogle Scholar
  27. P. Juluri, V. Tamarapalli, and D. Medhi. 2016. Measurement of quality of experience of video-on-demand services: A survey. IEEE Commun. Surv. Tutor. 18, 1 (2016), 401--418.Google ScholarGoogle ScholarCross RefCross Ref
  28. P. Juluri, V. Tamarapalli, and D. Medhi. 2016. QoE management in DASH systems using the segment aware rate adaptation algorithm. In Proceedings of the IEEE/IFIP Network Operations and Management Symposium. 129--136.Google ScholarGoogle Scholar
  29. S. Jumisko-Pyykkö, V. K. Malamal Vadakital, and M. M. Hannuksela. 2008. Acceptance threshold: A bidimensional research method for user-oriented quality evaluation studies. Int. J. Dig. Multimedia Broadcast. 2008, 712380 (2008), 1--20.Google ScholarGoogle Scholar
  30. D. Juszka and Z. Papir. 2015. A study on order effect in a subjective experiment on stereoscopic video quality. In Proceedings of the 7th International Workshop on Quality of Multimedia Experience (QoMEX’15). 1--6.Google ScholarGoogle Scholar
  31. S. S. Krishnan and R. K. Sitaraman. 2013. Video stream quality impacts viewer behavior: Inferring causality using quasi-experimental designs. IEEE/ACM Trans. Netw. 21, 6 (Dec. 2013), 2001--2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. J. Kua, G. Armitage, and P. Branch. 2017. A survey of rate adaptation techniques for dynamic adaptive streaming over HTTP. IEEE Commun. Surv. Tutor. 19, 3 (2017), 1842--1866.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. S. Lederer, C. Müller, and C. Timmerer. 2012. Dynamic adaptive streaming over HTTP dataset. In Proceedings of the 3rd ACM Multimedia Systems Conference (MMSys’12). 89--94. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. R. K. P. Mok, X. Luo, E. W. W. Chan, and R. K. C. Chang. 2012. QDASH: A QoE-aware DASH system. In Proceedings of the 3rd ACM Multimedia Systems Conference (MMSys’12). 11--22. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. E. Ozfatura, O. Ercetin, and H. Inaltekin. 2018. Optimal network-assisted multiuser DASH video streaming. IEEE Trans. Broadcast. 64, 2 (Jun. 2018), 247--265.Google ScholarGoogle ScholarCross RefCross Ref
  36. ITU-T Recommendation P.10/G.100. 2008. Vocabulary for Performance and Quality of Service. Amd. 2: New Definitions for Inclusion in Rec. ITU-T P.10/G.100. Retrieved from https://www.itu.int/rec/T-REC-P.10/.Google ScholarGoogle Scholar
  37. ITU-T Recommendation P.910. 2008. Subjective Video Quality Assessment Methods for Multimedia Applications. Retrieved from http://www.itu.int/rec/T-REC-P.910-200804-I.Google ScholarGoogle Scholar
  38. S. Petrangeli, J. Famaey, M. Claeys, S. Latré, and F. De Turck. 2015. QoE-driven rate adaptation heuristic for fair adaptive video streaming. ACM Trans. Multimedia Comput. Commun. Appl. 12, 2, Article 28 (Oct. 2015), 24 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. M. Ries, C. Crespi, O. Nemethova, and M. Rupp. 2007. Content based video quality estimation for H.264/AVC video streaming. In Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC’07). 2668--2673. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. H. Riiser, T. Endestad, P. Vigmostad, C. Griwodz, and P. Halvorsen. 2012. Video streaming using a location-based bandwidth-lookup service for bitrate planning. ACM Trans. Multimedia Comput. Commun. Appl. 8, 3 (2012), 24:1--24:19. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. H. Riiser, P. Vigmostad, C. Griwodz, and P. Halvorsen. 2013. Commute path bandwidth traces from 3G networks: Analysis and applications. In Proceedings of the 4th ACM Multimedia Systems Conference (MMSys’13). 114--118. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. I. Sodagar. 2011. The MPEG-DASH standard for multimedia streaming over the internet. IEEE MultiMedia 18, 4 (Apr. 2011), 62--67. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. W. Song and D. W. Tjondronegoro. 2014. Acceptability-based QoE models for mobile video. IEEE Trans. Multimedia 16, 3 (Apr. 2014), 738--750. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. T. Stockhammer. 2011. Dynamic adaptive streaming over HTTP - standards and design principles. In Proceedings of the 2nd ACM Multimedia Systems Conference (MMSys’11). 133--144. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. M. Watson. 2011. HTTP adaptive streaming in practice. In Proceedings of the ACM Multimedia Systems Conference.Google ScholarGoogle Scholar
  46. S. Xiang, L. Cai, and J. Pan. 2012. Adaptive scalable video streaming in wireless networks. In Proceedings of the 3rd ACM Multimedia Systems Conference (MMSys’12). 167--172. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. X. Yin, A. Jindal, V. Sekar, and B. Sinopoli. 2015. A control-theoretic approach for dynamic adaptive video streaming over HTTP. In Proceedings of ACM Special Interest Group on Data Communication (SIGCOMM’15). 325--338. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. S. Zhao and D. Medhi. 2017. SDN-assisted adaptive streaming framework for tile-based immersive content using MPEG-DASH. In Proceedings of the IEEE Conference on Network Function Virtualization and Software Defined Networks. 1--6.Google ScholarGoogle Scholar
  49. C. Zhou, C. W. Lin, and Z. Guo. 2016. mDASH: A markov decision-based rate adaptation approach for dynamic HTTP streaming. IEEE Trans. Multimedia 18, 4 (Apr. 2016), 738--751.Google ScholarGoogle ScholarCross RefCross Ref

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