DOI QR코드

DOI QR Code

Queueing Theoretic Approach to Playout Buffer Model for HTTP Adaptive Streaming

  • Park, Jiwoo (Department of Electronics and Communications Engineering, Kwangwoon University) ;
  • Chung, Kwangsue (Department of Electronics and Communications Engineering, Kwangwoon University)
  • Received : 2017.09.11
  • Accepted : 2018.04.09
  • Published : 2018.08.31

Abstract

HTTP-based adaptive streaming (HAS) has recently been widely deployed on the Internet. In the HAS system, a video content is encoded at multiple bitrates and the encoded video content is segmented into small parts of fixed durations. The HAS client requests a video segment and stores it in the playout buffer. The rate adaptation algorithm employed in HAS clients dynamically determines the video bitrate depending on the time-varying bandwidth. Many studies have shown that an efficient rate adaptation algorithm is critical to ensuring quality-of-experience in HAS systems. However, existing algorithms have problems estimating the network bandwidth because bandwidth estimation is performed on the client-side application stack. Without the help of transport layer protocols, it is difficult to achieve accurate bandwidth estimation due to the inherent segment-based transmission of the HAS. In this paper, we propose an alternative approach that utilizes the playout buffer occupancy rather than using bandwidth estimates obtained from the application layer. We start with a queueing analysis of the playout buffer. Then, we present a buffer-aware rate adaptation algorithm that is solely based on the mean buffer occupancy. Our simulation results show that compared to conventional algorithms, the proposed algorithm achieves very smooth video quality while delivering a similar average video bitrate.

Keywords

References

  1. "Cisco Visual Networking Index: Forecast and methodology, 2015-2020," Cisco System Inc., San Jose, CA, USA, Jun. 2016.
  2. T. Stockhammer, "Dynamic adaptive streaming over HTTP - Standards and design principles," in Proc. of the 2nd Annual ACM Conf. on Multimedia Systems., pp. 133-144, Feb. 2011.
  3. J. Roettgers, "Don't touch that dial: How YouTube is bringing adaptive streaming to mobile, TVs," Mar. 2013. [Online].
  4. S. Akhshabi, L. Anantakrishnan, A. C. Begen, and C. Dovrolis, "What happens when HTTP adaptive streaming players compete for bandwidth?," in Proc. of the 22nd Int. Workshop on Network and Operating System Support for Digital Audio and Video, pp. 9-14, Jun. 2012.
  5. 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 2012 Internet Measurement Conf., pp. 225-238, Nov. 2012. .
  6. L. D. Cicco, V. Caldaralo, V. Palmisano, and S. Mascolo, "ELASTIC: A client-side controller for dynamic adaptive streaming over HTTP (DASH)," in Proc. of Int. Packet Video Workshop, pp. 1-8, Dec. 2013. .
  7. S. Akhshabi, L. Anantakrishnan, C. Dovrolis, and A. C. Began, "Server-based traffic shaping for stabilizing oscillating adaptive streaming players," in Proc. of the 23rd Int. Workshop on Network and Operating System Support for Digital Audio and Video, pp. 19-24, Feb. 2013.
  8. X. Zhu, Z. Li, R. Pan, J. Gahm, and H. Hu, "Fixing multi-client oscillations in HTTP-based adaptive streaming: A control theoretic approach," in Proc. of IEEE Int. Workshop on Multimedia Signal Processing, pp. 230-235, Oct. 2013. .
  9. S. Akhshabi, A. C. Begen, and C. Dovrolis, "An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP," in Proc. of the 2nd Annual ACM Conf. on Multimedia Systems, pp. 157-168, Feb. 2011. .
  10. J. He, Z. Xue, D. Wu, D. O. Wu, and Y. Wen, "CBM: Online strategies on cost-aware buffer management for mobile video streaming," IEEE Transactions on Multimedia, vol. 16, no. 1, pp. 242-252, Jan. 2014. https://doi.org/10.1109/TMM.2013.2284894
  11. Y. Xu, E. Altman, R. El-Azouzi, M. Haddad, S. Elayoubi, and T. Jimenez, "Analysis of buffer starvation with application to objective QoE optimization of streaming services," IEEE Transactions on Multimedia, vol. 16, no. 3, pp. 813-827, Apr. 2014. . https://doi.org/10.1109/TMM.2014.2300041
  12. L. D. Cicco, S. Mascolo, and V. Palmisano, "Feedback control for adaptive live video streaming," in Proc. of the 2nd Annual ACM Conf. on Multimedia Systems, pp. 145-156, Feb. 2011.
  13. E. C. R. Mok, X. Luo, and R. Chang, "QDASH: A QoE-aware DASH system," in Proc. of the 3rd Multimedia Systems Conf., pp. 11-22, Feb. 2012. .
  14. D. Jarnikov and T. Ozcelebi, "Client intelligence for adaptive streaming solutions," Signal Processing: Image Communication, vol. 26, no. 7, pp. 378-389, Aug. 2011. https://doi.org/10.1016/j.image.2011.03.003
  15. A. Bokani, M. Hassan, S. Kanhere, and X. Zhu, "Optimizing HTTP-based adaptive streaming in vehicular environment using markov decision process," IEEE Transactions on Multimedia, vol. 17, no. 12, pp. 2297-2309, Dec. 2015. . https://doi.org/10.1109/TMM.2015.2494458
  16. C. Zhou, X. Zhang, L. Huo, and Z. Guo, "A control-theoretic approach to rate adaptation for dynamic HTTP streaming," in Proc. of IEEE Visual Communications and Image Processing, pp. 1-6, Nov. 2012. .
  17. J. Jiang, V. Sekar, and H. Zhang, "Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE," in Proc. of the 8th Int. Conf. on Emerging Networking Experiments and Technologies, pp. 97-108, Dec. 2012. .
  18. H. Mao, R. Netravali, and M. Alizadeh, "Neural adaptive video streaming with pensieve," in Proc. of the ACM Conf. on SIGCOMM, pp. 197-210, Aug. 2017. .
  19. K. Miller, A. Al-Tamimi, and A. Wolisz, "QoE-based low-delay live streaming using throughput predictions," ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 13, no. 1, Oct. 2016. .
  20. Z. Zhu, S. Li, and X. Chen, "Design QoS-aware multi-path provisioning strategies for efficient cloud-assisted SVC video streaming to heterogeneous clients," IEEE Transactions on Multimedia, vol. 15, no. 4, pp. 758-768, Jun. 2013. . https://doi.org/10.1109/TMM.2013.2238908
  21. Z. Li, X. Zhu, J. Gahm, R. Pan, H. Hu, A. C. Began, and D. Oran, "Probe and adapt: 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
  22. T. Huang, R. Johari, N. McKeown, M. Trunnell, and M. Watson, "A buffer-based approach to rate adaptation: Evidence from a large video streaming service," in Proc. of the 2014 ACM Conf. on SIGCOMM, pp. 187-198, Aug. 2014. .