Abstract
Traditionally, video adaptive algorithms aim to select the representation that better fits to the current download rate. In recent years, a number of new approaches appeared that take into account the buffer occupancy and the probability of video rebuffering as important indicators of the representation to be selected. We propose an optimization of the existing algorithm based on rebuffering probability and argue that the algorithm should avoid the situations when the client buffer is full and the download is stopped, since these situations decrease the efficiency of the algorithm. Reducing full buffer states does not increase the rebuffering probability thanks to a clever management of the client buffer, which analyses the buffer occupancy and downloads higher bitrate representations only in the case of high buffer occupancy.
- S. Akhsabi, A. 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 Series (MMSys’11), New York. Google Scholar
Digital Library
- C. Alberti et al. 2013. Automated QoE evaluation of dynamic adaptive streaming over HTTP. In Proceedings of the 5th International Workshop on Quality of Multimedia Experience (QoMEX’13).Google Scholar
Cross Ref
- S. Asmussen, J. L. Jensen, and L. Rojas-Nandayapa. 2014. On the laplace transform of the lognormal distribution. Methodology and Computing in Applied Probability, Springer.Google Scholar
- H. Balakrishnan, S. Seshan, M. Stemm, and R. H. Katz. 1997. Analyzing stability in wide-area network performance. In Proceedings of ACM SIGMETRICS. Google Scholar
Digital Library
- J. M. Batalla. 2015. Advanced multimedia service provisioning based on efficient interoperability of adaptive streaming protocol and high efficient video coding. Springer J. Real-Time Image Process.Google Scholar
- Bęben Andrzej, Wiśniewski Piotr, Mongay Batalla Jordi, and Krawiec Piotr. May 2016. ABMA +: lightweight and efficient algorithm for HTTP adaptive streaming. In Proceedings of the ACM Multimedia Systems Conference Series (MMSys’16). Google Scholar
Digital Library
- D. Bezerra, M. Ito, W. Melo, D. Sadok, and J. Kelner. 2016. DBuffer: A state machine oriented control system for DASH. In Proceedings of the IEEE Symposium on Computers and Communication (ISCC’16).Google Scholar
- F. Chiariotti, S. D’Aronco, L. Toni, and P. Frossard. 2016. Online learning adaptation strategy for DASH clients. In Proceedings of the ACM Multimedia Systems Conference Series (MMSys’16). Google Scholar
Digital Library
- L. D. Cicco, S. Mascolo, and V. Palmisano. 2011. Feedback control for adaptive live video streaming. In Proceedings of the ACM Multimedia Systems Conference Series (MMSys’11). Google Scholar
Digital Library
- L. D. Cicco. et al. 2013. ELASTIC: A client-side controller for dynamic adaptive streaming over HTTP (DASH). Proceedings of the IEEE Packet Video Workshop.Google Scholar
- DASH-IF. 2015. Guidelines for implementation: DASH-AVC/264 test cases and vectors.” Retyrieved from http://dashif.org/wp-content/uploads/2015/04/dash-avc-264-test-vectors-v09-communityreview.pdf.Google Scholar
- W. Feller. 1971. An Introduction to Probability Theory and Its Applications, Vol. II, 2nd edition, Wiley.Google Scholar
- T. Huang, R. Johari, and McKeown N. Downton. 2013. Abbey without the hippcus: Buffer-based rate adaptation for HTTP video streaming. Proceedings of the ACM FhMN Workshop. SIGCOMM, Hong Kong. Google Scholar
Digital Library
- Huang Te-Yuan et al. 2014. A buffer-based approach to rate adaptation: Evidence from a large video streaming service. In Proceedings of ACM SIGCOMM. Google Scholar
Digital Library
- T. Hung Le et al. 2013. Buffer-based bitrate adaptation for adaptive HTTP streaming. In Proceedings of the International Conference on Advanced Technologies for Communications (ATC’13).Google Scholar
- J. Jiang, V. Sekar, and H. Zhang. 2012. Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE. Proceedings of ACM CoNEXT. Google Scholar
Digital Library
- K. Kim and H. A. Latchman. 2009. Statistical traffic modeling of MPEG frame size experiments and analysis. Journal of Systemics, Cybernetics and Informatics 7, 6 (2009), 54--59.Google Scholar
- C. Liu, I. Bouazizi, and M. Gabbouj. 2011. Rate adaptation for adaptive http streaming. In Proceedings of the ACM Multimedia Systems Conference Series (MMSys’11). Google Scholar
Digital Library
- V. A. Memos and K. E. Psannis. 2015. Encryption algorithm for efficient transmission of HEVC media. J. Real-Time Image Process. 12: 473. Google Scholar
Digital Library
- K. Miller et al. 2012. Adaptation algorithm for adaptive streaming over HTTP, In Proceedings of the Packet Video Workshop.Google Scholar
Cross Ref
- R. Mok, X. Luo, E. Chan, and R. Chang. 2012. QDASH: A QoE-aware DASH system. In Proceedings of the ACM Multimedia Systems Conference Series (MMSys’12). Google Scholar
Digital Library
- Mongay Batalla Jordi, Krawiec Piotr, Bęben Andrzej, Wiśniewski Piotr, and Chydziński Andrzej. 2016. Adaptive video streaming: rate and buffer on the track of minimum re-buffering. IEEE J. Select. Areas Commun.. Vol 34. Issue 8. Pages: 1--14 Google Scholar
Digital Library
- C. Muller, S. Lederer, and C. Timmerer. 2012. An evaluation of dynamic adaptive streaming over HTTP in vehicular environments. In Proceedings of the 4th Workshop on Mobile Video. Google Scholar
Digital Library
- O. Oyman and S. Singh April. 2012. Quality of experience for HTTP adaptive streaming services. IEEE Commun. Mag., 50, 4, 20--27.Google Scholar
Cross Ref
- K. E. Psannis. 2015. HEVC in wireless environments. J. Real-Time Image Process. 12, 509. Google Scholar
Digital Library
- Riiser Haakon, Vigmostad Paul, Griwodz Carsten, and Halvorsen Pål. 2013. Commute path bandwidth traces from 3g networks: Analysis and applications. In Proceedings of the ACM Multimedia Systems Conference Series (MMSys’13). Google Scholar
Digital Library
- K. Salah, F. Al-Haidari, M. H. Omar, and A. Chaudhry. 2011. Statistical analysis of H.264 video frame size distribution. Communications, IET, 5, 14.Google Scholar
Cross Ref
- Y. Sanchez et al. 2012 Efficient HTTP-based streaming using scalable video coding. Signal Process.: Image Commun.. 27, 4 Google Scholar
Digital Library
- M. Seufert et al. 2015. A survery on quality of experience of HTTP adaptive streaming. IEEE Commun. Surveys Tutor.Google Scholar
- K. Spiteri, R. Urgaonkar, and R. K. Sitaraman. 2016. BOLA: Near-optimal bitrate adaptation for online videos. Proceedings of IEEE INFOCOM. arXiv:1601.06748.Google Scholar
- T. C. Thang et al. Feb. 2012. Adaptive streaming of audiovisual content using MPEG DASH. IEEE Trans. Consum. Electron. 58, 1, 78--85.Google Scholar
Cross Ref
- G. Tian and Y. Liu. 2012. Towards agile and smooth video adaptation in dynamic HTTP streaming. In Proceedings of ACM CoNEXT. Google Scholar
Digital Library
- Wiśniewski Piotr, Beben Andrzej, Mongay Batalla Jordi, and Krawiec Piotr. 2015. On delimiting video rebuffering for stream switching adaptive applications. In Proceedings of the IEEE International Conference on Communications (ICC’15).Google Scholar
- X. Yin, V. Sekar, and B. Sinopoli. 2014. Toward a principled framework to design dynamic adaptive streaming. Algorithms over HTTP: Proceedings of the 13th ACM Workshop on Hot Topics in Networks. Google Scholar
Digital Library
- Y. Zhang, L. Breslau, V. Paxson, and S. Shenker. 2002. On the characteristics and origins of internet flow rates. In Proceedings of ACM SIGCOMM. Google Scholar
Digital Library
Index Terms
On Optimizing Adaptive Algorithms Based on Rebuffering Probability
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