Advertisement

A Joint Bitrate and Buffer Control Scheme for Low-Latency Live Streaming

  • Si Chen
  • Yuan ZhangEmail author
  • Huan Peng
  • Jinyao Yan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11936)

Abstract

Live video streaming has experienced explosive growth on the mobile Internet. Unlike on-demand streaming, live video streaming faces more challenges due to the strong requirement of low latency. To balance several inherently conflicting performance metrics and improve the overall quality of experience (QoE), the adaptive bitrate algorithm is widely used under time-varying network conditions. However, it does not perform well at low latency. In this paper, we present a joint bitrate and buffer control scheme (JBBC) for low-latency live streaming based on latency-constrained bitrate adaptation and playback rate adaptation. Experiments demonstrate that the proposed algorithm has better performance on overall QoE than most existing adaptive schemes, achieving a more stable bitrate selection and relatively lower delay on the premise of almost no rebuffering.

Keywords

Live video streaming ABR (adaptive bitrate algorithm) AMP (adaptive media playout) QoE (quality of experience) 

Notes

Acknowledgments

This work was supported by National Natural Science Foundation of China (61472389).

References

  1. 1.
    Cisco: Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2016–2021 White Paper (2017)Google Scholar
  2. 2.
    Li, Z., Zhu, X., Gahm, J., et al.: Probe and adapt: rate adaptation for HTTP video streaming at scale. IEEE J. Sel. Areas Commun. 32(4), 719–733 (2014)CrossRefGoogle Scholar
  3. 3.
    Yin, X., Jindal, A., Sekar, V., et al.: A control-theoretic approach for dynamic adaptive video streaming over HTTP. In: ACM Conference on Special Interest Group on Data Communication, pp. 325–338. ACM (2015)Google Scholar
  4. 4.
    Mao, H., Netravali, R., Alizadeh, M.: Neural adaptive video streaming with pensieve. In: Proceedings of the Conference of the ACM Special Interest Group on Data Communication, pp. 197–210. ACM (2017)Google Scholar
  5. 5.
    Huang, T.Y., et al.: A buffer-based approach to rate adaptation: evidence from a large video streaming service. In: SIGCOMM. ACM (2014) Google Scholar
  6. 6.
    Wei, S., Swaminathan, V.: Low latency live video streaming over HTTP 2.0. In: Proceedings of the Network and Operating System Support on Digital Audio and Video Workshop, p. 37. ACM (2014)Google Scholar
  7. 7.
    van der Hooft, J., De Boom, C., Petrangeli, S., Wauters, T., De Turck, F.: An HTTP/2 push-based framework for low-latency adaptive streaming through user profiling. In: NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium, Taipei, pp. 1–5 (2018)Google Scholar
  8. 8.
    Wang, J., Meng, S., Sun, J., Quo, Z.: A general PID-based rate adaptation approach for TCP-based live streaming over mobile networks. In: Proceedings of the IEEE ICME, Seattle, USA, pp. 1–6, July 2016Google Scholar
  9. 9.
    Xie, L., Zhou, C., Zhang, X.: Dynamic threshold based rate adaptation for HTTP live streaming. In: Proceedings of the IEEE ISCAS, Baltimore, MD, USA, pp. 1–4, May 2017Google Scholar
  10. 10.
    Zhang, G., Lee, J.Y.B.: LAPAS: latency-aware playback-adaptive streaming (2019, in press)Google Scholar
  11. 11.
  12. 12.
    Chen, Y., Liu, G.: Adaptive media playout assisted rate adaptation scheme for HTTP adaptive streaming over lte system. In: 2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6. IEEE (2016)Google Scholar
  13. 13.
    Su, Y.F., Yang, Y.H., Lu, M.T., et al.: Smooth control of adaptive media playout for video streaming. IEEE Trans. Multimed. 11(7), 1331–1339 (2009)CrossRefGoogle Scholar
  14. 14.
    Li, M., Yeh, C.L., Lu, S.Y.: Real-time QoE monitoring system for video streaming services with adaptive media playout. Int. J. Digit. Multimed. Broadcast. (2018) Google Scholar
  15. 15.
    Kalman, M., Steinbach, E., Girod, B.: Adaptive media playout for low-delay video streaming over error-prone channels. IEEE Trans. Circuits Syst. Video Technol. 14(6), 841–851 (2004)CrossRefGoogle Scholar
  16. 16.
    Fan, M., Yang, J., Zhao, Y.: Probability estimation based adaptive media playout algorithm. In: 2010 2nd International Conference on Advanced Computer Control, vol. 4, pp. 267–271. IEEE (2010)Google Scholar
  17. 17.
    Chuang, H.C., Huang, C.Y., Chiang, T.: Content-aware adaptive media playout controls for wireless video streaming. IEEE Trans. Multimed. 9(6), 1273–1283 (2007)CrossRefGoogle Scholar
  18. 18.
    Ou, Y.F., Liu, T., Zhao, Z., et al.: Modeling the impact of frame rate on perceptual quality of video. In: 2008 15th IEEE International Conference on Image Processing, pp. 689–692. IEEE (2008)Google Scholar
  19. 19.
    Alsrehin, N.O., Klaib, A.F.: VMQ: an algorithm for measuring the video motion quality. Bull. Electr. Eng. Inform. 8(1), 231–238 (2019)Google Scholar
  20. 20.
    Mok, R.K.P., Luo, X., Chan, E.W.W., Chang, R.K.C.: QDASH: a QoE-aware DASH system. In: Proceedings of the Annual ACM SIGMM Conference on Multimedia Systems (MMSys 2012), pp. 11–22 (2012)Google Scholar
  21. 21.
  22. 22.
    Mnih, V., Badia, A.P., Mirza, M., et al.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016)Google Scholar
  23. 23.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (2018)zbMATHGoogle Scholar
  24. 24.
    Riiser, H., Endestad, T., Vigmostad, P., et al.: Video streaming using a location-based bandwidth-lookup service for bitrate planning. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 8(3), 24 (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Communication University of ChinaBeijingChina

Personalised recommendations