Ashkan Sobhani
Shervin Shirmohammadi
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The Hypertext Transfer Protocol (HTTP) Adaptive Streaming (HAS) has now become ubiquitous and accounts for a large amount of video delivery over the Internet. But since the Internet is prone to bandwidth variations, HAS's up and down switching between different video bitrates to keep up with bandwidth variations leads to a reduction in Quality of Experience (QoE). In this article, we propose a video bitrate adaptation and prediction mechanism based on Fuzzy logic for HAS players, which takes into consideration the estimate of available network bandwidth as well as the predicted buffer occupancy level in order to proactively and intelligently respond to current conditions. This leads to two contributions: First, it allows HAS players to take appropriate actions, sooner than existing methods, to prevent playback interruptions caused by buffer underrun, reducing the ON-OFF traffic phenomena associated with current approaches and increasing the QoE. Second, it facilitates fair sharing of bandwidth among competing players at the bottleneck link. We present the implementation of our proposed mechanism and provide both empirical/QoE analysis and performance comparison with existing work. Our results show that, compared to existing systems, our system has (1) better fairness among multiple competing players by almost 50% on average and as much as 80% as indicated by Jain's fairness index and (2) better perceived quality of video by almost 8% on average and as much as 17%, according to the estimate the Mean Opinion Score (eMOS) model.
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- S. Akhshabi, L. Anantakrishnan, A. C. Begen, and C. Dovrolis. 2012a. What happens when HTTP adaptive streaming players compete for bandwidth? In Proceedings of the 22nd International Workshop on Network and Operating System Support for Digital Audio and Video. ACM, 9--14. Google Scholar
Digital Library
- S. Akhshabi, S. Narayanaswamy, A. C. Begen, and C. Dovrolis. 2012b. An experimental evaluation of rate-adaptive video players over HTTP. Sign. Process.: Image Commun. 27, 271--287. Google ScholarDigital Library
- C. Alberti, D. Renzi, C. Timmerer, C. Mueller, S. Lederer, S. Battista, and M. Mattavelli. 2013. Automated QoE evaluation of dynamic adaptive streaming over HTTP. In Proceedings of the 2013 5th International Workshop on Quality of Multimedia Experience (QoMEX). IEEE, 58--63.Google Scholar
- A. Beben, P. Wiśniewski, J. M. Batalla, and P. Krawiec. 2016. ABMA+: lightweight and efficient algorithm for HTTP adaptive streaming. In Proceedings of the 7th ACM International Conference on Multimedia Systems 2016 May 10 (2). Google ScholarDigital Library
- A. C. Begen, T. Akgssul, and M. Baugher. 2011. Watching video over the web: Part 1: Streaming protocols. IEEE Internet Comput. 15, 2, 54--63. Google ScholarDigital Library
- N. Bouten, R. D. O. 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, 96--115. Google ScholarDigital Library
- R. G. Brown. 1957. Exponential smoothing for predicting demand. In Operations Research, INST Operations Research Management Sciences, 145--145.Google Scholar
- S. Carmel, T. Daboosh, E. Reifman, N. Shani, Z. Eliraz, D. Ginsberg, and E. Ayal. 2002. Network media streaming. US Patent 6, 389--473.Google Scholar
- C. Chatfield. 1978. The holt-winters forecasting procedure. Appl. Stat. 264--279.Google Scholar
- C. Chen, L. K. Choi, G. De veciana, C. Caramanis, R. W. Heath, and A. C. Bovik. 2014. Modeling the time—varying subjective quality of HTTP video streams with rate adaptations. IEEE Trans. Image Process. 23, 2206--2221.Google ScholarCross Ref
- M. Claeys, S. Latre, J. Famaey, and F. de Turck. 2014. Design and evaluation of a self-learning HTTP adaptive video streaming client. IEEE Commun. Lett. 2014 Apr; 18, 4, 716--9.Google Scholar
- W. J. Conover. 1980. Practical Nonparametric Statistics. Wiley and Sons, 99--104.Google Scholar
- I. D. Curcio, V. K. M. Vadakital, and M. M. Hannuksela. 2010. Geo-predictive real-time media delivery in mobile environment. In Proceedings of the 3rd Workshop on Mobile Video Delivery. ACM, 3--8. Google ScholarDigital Library
- J. De vriendt, D. De vleeschauwer, and D. Robinson. 2013. Model for estimating QoE of video delivered using HTTP adaptive streaming. In Proceedings of the 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013). IEEE, 1288--1293.Google Scholar
- Echostar. Retrieved from http://www.echostar.com/ProductsAndServices/VideoAndBroadcast/Delivery/Technology.aspx, 2009.Google Scholar
- J. Hao, R. Zimmermann, and H. Ma. 2014. Gtube: Geo-predictive video streaming over http in mobile environments. In Proceedings of the 5th ACM Multimedia Systems Conference. ACM, 259--270. Google ScholarDigital Library
- R. Houdaille and S. Gouache. 2012. Shaping http adaptive streams for a better user experience. In Proceedings of the 3rd Multimedia Systems Conference. ACM, 1--9. Google ScholarDigital Library
- 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 2014 ACM Conference on SIGCOMM. ACM, 187--198. Google ScholarDigital Library
- Iperf. Retrieved from http://iperf.sourceforge.net/, 2005.Google Scholar
- R. Jain, D. M. Chiu, and W. R. Hawe. 1984. A Quantitative Measure of Fairness and Discrimination for Resource Allocation in Shared Computer System. Digital Equipment Corporation, Eastern Research Laboratory, Hudson, MA.Google Scholar
- J. Jansen, T. Coppens, and D. de Vleeschauwer. 2002. Quality assessment of video streaming in the broadband era. In Proceedings of the Workshop on Advanced Concepts for Intelligent Vision Systems (ACIVS’02). 38--45.Google Scholar
- 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, 2014, 326--340. Google ScholarDigital Library
- J. Jiang, V. Sekar, and Y. Sun. 2015. DDA: Cross-session throughput prediction with applications to video bitrate selection. arXiv preprint arXiv:1505.02056. 2015 May 8.Google Scholar
- V. Joseph and G. De veciana. 2014. NOVA: QoE-driven optimization of DASH-based video delivery in networks. In 2014 Proceedings INFOCOM. IEEE, 82--90.Google Scholar
- P. Juluri, V. Tamarapalli, and D. Medhi. 2015. Look-ahead rate adaptation algorithm for DASH under varying network environments. In Proceedings of the 11th International Conference on the InDesign of Reliable Communication Networks (DRCN). 89--90.Google Scholar
- 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 (ICCW). 1765--1770.Google Scholar
- P. Kaufman. 1995. Smarter Trading. McGraw-Hill, New York, NY.Google Scholar
- R. Kuschnig, I. Kofler, and H. Hellwagner. 2011. Evaluation of HTTP-based request-response streams for internet video streaming. In Proceedings of the 2nd Annual ACM Conference on Multimedia Systems. ACM, 245--256. Google ScholarDigital Library
- H. T. Le, D. V. Nguyen, N. P. Ngoc, A. T. Pham, and T. C. Thang. 2013. Buffer-based bitrate adaptation for adaptive HTTP streaming. In Proceedings of the 2013 International Conference on Advanced Technologies for Communications (ATC). IEEE, 33--38.Google Scholar
- 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 5th ACM Multimedia Systems Conference. ACM, 248--258. Google ScholarDigital Library
- Z. Li, X. Zhu, J. Gahm, R. Pan, H. Hu, A. Begen, and D. Oran. 2014b. Probe and adapt: Rate adaptation for http video streaming at scale. IEEE J. Select. Areas Commun. 32, 719--733.Google ScholarCross Ref
- B. Li, Z. Wang, J. Liu, and W. Zhu. 2013. Two decades of internet video streaming: A retrospective view. ACM Trans. Multimedia Comput. Commun. Appl. 9, 33. Google ScholarDigital Library
- Y. Lir, S. Dey, D. Gillies, F. Ulupinar, and M. Luby. 2013. User experience modeling for DASH video. In Proceedings of the 20th International Packet Video Workshop 2013. 1--8.Google Scholar
- C. Liu, I. Bouazizi, and M. Gabbouj. 2011. Rate adaptation for adaptive HTTP streaming. In Proceedings of the Second Annual ACM Conference on Multimedia Systems. ACM, 169--174. Google ScholarDigital Library
- S. Liu and J. Y. L. Forrest. 2010. Grey Systems: Theory and Applications. Springer.Google ScholarCross Ref
- X. Liu. 2007. Parameterized defuzzification with maximum entropy weighting function—another view of the weighting function expectation method. Math. Compu. Model. 45, 177--188. Google ScholarDigital Library
- V. Mart and N. Garc. 2016. Evaluation of q-learning approach for HTTP adaptive streaming. In IEEE International Conference on Consumer Electronics (ICCE 2016). 293--294.Google Scholar
- K. Miller, E. Quacchio, G. Gennari, and A. Wolisz. 2012. Adaptation algorithm for adaptive streaming over HTTP. In Proceedings of the 2012 19th International Packet Video Workshop (PV). IEEE, 173--178.Google Scholar
- R. K. Mok, E. W. Chan, and R. K. Chang. 2011. Measuring the quality of experience of HTTP video streaming. In Proceedings of the 2nd Annual ACM Conference on Multimedia Systems. ACM, 245--256. 485--492.Google Scholar
- R. K. Mok, X. Luo, E. W. Chan, and R. K. Chang. 2012a. QDASH: A QoE-aware DASH system. In Proceedings of the 3rd Multimedia Systems Conference. ACM, 11--22. Google ScholarDigital Library
- (MPEG) IJSW. 2010. Dynamic adaptive streaming over http. w11578, CD 23001-6, w11578, CD 23001-6. ISO/IEC JTC 1/SC 29/WG 11 (MPEG), Guangzhou, China.Google Scholar
- E. H. Mamdani. 1974. Application of fuzzy algorithms for control of simple dynamic plant. In Proceedings of the Institution of Electrical Engineers (IET). 1585--1588.Google ScholarCross Ref
- J. Nagle. 1984. Congestion control in IP/TCP internetworks. (Jan. 1984). http://tools.ietf.org/html/rfc896. Google ScholarDigital Library
- Netflix. Retrieved from https://www.netflix.com/ca/, 2016.Google Scholar
- J. Park and K. Chung. 2015. Rate adaptation scheme for HTTP-based streaming to achieve fairness with competing TCP traffic. In Proceedings of the 2015 International Conference on Information Networking (ICOIN’15). IEEE, 222--226.Google Scholar
- S. Petrangeli, J. Famaey, M. Claeys, S. Latré, and F. D. Turck. 2015. Qoe-driven rate adaptation heuristic for fair adaptive video streaming. ACM Trans Multimedia Comput. Commun. Appl. 12, 28. Google ScholarDigital Library
- Red Bull Playstreets. 2014. {Online}. Available: http://www-itec.uniklu.ac.at/ftp/datasets/mmsys12/RedBullPlayStreets/redbull2s/.Google Scholar
- 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, 24. Google ScholarDigital Library
- L. Rizzo. 1997. Dummynet: A simple approach to the evaluation of network protocols. ACM SIGCOMM Comput. Commun. Rev. 27, 31--41. Google ScholarDigital Library
- A. J. Smola and B. Schölkopf. 2004. A tutorial on support vector regression. Stat. Comput. 14, 199--222. Google ScholarDigital Library
- A. Sobhani, A. Yassine, and S. Shirmohammadi. 2015. A fuzzy-based rate adaptation controller for DASH. In Proceedings of the 25th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video. ACM, 31--36. Google ScholarDigital Library
- K. Spiteri, R. Urgaonkar, and R. K. Sitaraman BOLA: Near-optimal bitrate adaptation for online videos. In Proceedings of the 35th Annual IEEE International Conference on Computer Communications. 1--9.Google Scholar
- T. C. Thang, H. T. Le, A. T. Pham, and Y. M. Ro. 2014. An evaluation of bitrate adaptation methods for HTTP live streaming. IEEE J. Select. Areas Commun. 32, 693--705.Google ScholarCross Ref
- T. C. Thang, Q. Ho, J. W. Kang, and A. T. Pham. 2012. Adaptive streaming of audiovisual content using MPEG DASH. IEEE Trans. Consumer Electron. 58, 78--85.Google ScholarCross Ref
- G. Tian and Y. Liu. 2012. Towards agile and smooth video adaptation in dynamic HTTP streaming. In Proceedings of the 8th International Conference on Emerging Networking Experiments and Technologies. ACM, 109--120. Google ScholarDigital Library
- L. Toni, R. Aparicio-pardo, G. Simon, A. Blanc, and P. Frossard. 2014. Optimal set of video representations in adaptive streaming. In Proceedings of the 5th ACM Multimedia Systems Conference, 2014, March. 271--282. Google ScholarDigital Library
- P. Xiong, J. Shen, Q. Wang, D. Jayasinghe, J. Li, and C. Pu. 2012. NBS: A network-bandwidth-aware streaming version switcher for mobile streaming applications under fuzzy logic control. In Proceedings of the 2012 IEEE 1st International Conference on Mobile Services (MS). IEEE, 48--55. Google ScholarDigital Library
- J. Yao, S. S. Kanhere, and M. Hassan. 2012. Improving QoS in high-speed mobility using bandwidth maps. IEEE Trans. Mobile Comput. 11, 603--617. Google ScholarDigital Library
- X. Yin, V. Jindal, and A. B. A Sekar Sinopoli. 2015. control-theoretic approach for dynamic adaptive video streaming over HTTP. ACM SIGCOMM Comput. Commun. Rev. 45, 4 (Sep. 2015), 325--38. Google ScholarDigital Library
- I. T. Young. 1977. Proof without prejudice: Use of the Kolmogorov-Smirnov test for the analysis of histograms from flow systems and other sources. J Histochem. Cytochem. 25, 1977, 935--941.Google ScholarCross Ref
- L. A. Zadeh. 1975. The concept of a linguistic variable and its application to approximate reasoning—I. Inf. Sci. 8, 199--249.Google ScholarCross Ref
- A. Zambelli. 2009. IIS smooth streaming technical overview. Microsoft Corporation 25, 3, 40.Google Scholar
- C. Zhou and C. W. LIN. 2015. A markov decision based rate adaption approach for dynamic HTTP streaming. Vis. Commun. Image Processing (VCIP 2015). 1--4.Google Scholar
- C. Zhou, C. Lin, X. Zhang, and Z. Guo. 2013. Buffer-based smooth rate adaptation for dynamic HTTP streaming. In Proceedings of the Signal and Information Processing Association Annual Summit and Conference (APSIPA’13). IEEE, 1--9.Google Scholar
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