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Understanding Video Streaming Algorithms in the Wild

  • Melissa Licciardello
  • Maximilian GrünerEmail author
  • Ankit Singla
Conference paper
  • 37 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12048)

Abstract

While video streaming algorithms are a hot research area, with interesting new approaches proposed every few months, little is known about the behavior of the streaming algorithms deployed across large online streaming platforms that account for a substantial fraction of Internet traffic. We thus study adaptive bitrate streaming algorithms in use at 10 such video platforms with diverse target audiences. We collect traces of each video player’s response to controlled variations in network bandwidth, and examine the algorithmic behavior: how risk averse is an algorithm in terms of target buffer; how long does it takes to reach a stable state after startup; how reactive is it in attempting to match bandwidth versus operating stably; how efficiently does it use the available network bandwidth; etc. We find that deployed algorithms exhibit a wide spectrum of behaviors across these axes, indicating the lack of a consensus one-size-fits-all solution. We also find evidence that most deployed algorithms are tuned towards stable behavior rather than fast adaptation to bandwidth variations, some are tuned towards a visual perception metric rather than a bitrate-based metric, and many leave a surprisingly large amount of the available bandwidth unused.

References

  1. 1.
  2. 2.
    Hulu terms of use. https://www.hulu.com/terms
  3. 3.
  4. 4.
  5. 5.
  6. 6.
    Akhtar, Z., et al.: Oboe: auto-tuning video ABR algorithms to network conditions. In: ACM SIGCOMM (2018)Google Scholar
  7. 7.
    Añorga, J., Arrizabalaga, S., Sedano, B., Goya, J., Alonso-Arce, M., Mendizabal, J.: Analysis of YouTube’s traffic adaptation to dynamic environments. Multimedia Tools Appl. 77(7), 7977 (2018)CrossRefGoogle Scholar
  8. 8.
    De Cicco, L., Caldaralo, V., Palmisano, V., Mascolo, S.: Elastic: a client-side controller for dynamic adaptive streaming over HTTP (DASH). In: IEEE Packet Video Workshop (PV) (2013)Google Scholar
  9. 9.
    Federal Communications Commission: Validated data September 2017 - measuring broadband America. https://www.fcc.gov/reports-research/reports/
  10. 10.
    Ghasemi, M., Kanuparthy, P., Mansy, A., Benson, T., Rexford, J.: Performance characterization of a commercial video streaming service. In: ACM IMC (2016)Google Scholar
  11. 11.
    Grüner, M., Licciardello, M.: Understanding video streaming algorithms in the wild - scripts. https://github.com/magruener/understanding-video-streaming-in-the-wild
  12. 12.
    van der Hooft, J., et al.: HTTP/2-based adaptive streaming of HEVC video over 4G/LTE networks. IEEE Commun. Lett. 20(11), 2177–2180 (2016)CrossRefGoogle Scholar
  13. 13.
    Jiang, J., Sekar, V., Zhang, H.: Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with festive. IEEE/ACM Trans. Netw. 22(1), 326–340 (2014).  https://doi.org/10.1109/TNET.2013.2291681CrossRefGoogle Scholar
  14. 14.
    Li, Z., et al.: Probe and adapt: rate adaptation for HTTP video streaming at scale. IEEE J. Sel. Areas Commun. 32(4), 719–733 (2014).  https://doi.org/10.1109/JSAC.2014.140405CrossRefGoogle Scholar
  15. 15.
    Li, Z., Aaron, A., Katsavounidis, I., Moorthy, A., Manohara, M.: Toward a practical perceptual video quality metric (2016). https://medium.com/netflix-techblog/toward-a-practical-perceptual-video-quality-metric-653f208b9652
  16. 16.
    Mao, H., et al.: Real-world video adaptation with reinforcement learning. In: Reinforcement Learning for Real Life (ICML workshop) (2019)Google Scholar
  17. 17.
    Mao, H., Netravali, R., Alizadeh, M.: Neural adaptive video streaming with pensieve. In: ACM SIGCOMM, pp. 197–210. ACM (2017)Google Scholar
  18. 18.
    Miller, K., Bethanabhotla, D., Caire, G., Wolisz, A.: A control-theoretic approach to adaptive video streaming in dense wireless networks. IEEE Trans. Multimedia 17(8), 1309–1322 (2015)Google Scholar
  19. 19.
    Mondal, A., et al.: Candid with YouTube: adaptive streaming behavior and implications on data consumption. In: ACM NOSSDAV (2017)Google Scholar
  20. 20.
    Moreau, E.: What Is Vimeo? An Intro to the Video Sharing Platform. https://www.lifewire.com/what-is-vimeo-3486114
  21. 21.
    Pantos, R., May, W.: HTTP Live Streaming Draft. https://tools.ietf.org/html/draft-pantos-http-live-streaming-17.html
  22. 22.
    Qin, Y., et al.: ABR streaming of VBR-encoded videos: characterization, challenges, and solutions. In: ACM CoNEXT (2018)Google Scholar
  23. 23.
    Qin, Y., et al.: A control theoretic approach to ABR video streaming: a fresh look at PID-based rate adaptation. In: INFOCOM 2017-IEEE Conference on Computer Communications, IEEE, pp. 1–9. IEEE (2017)Google Scholar
  24. 24.
    Riiser, H., Vigmostad, P., Griwodz, C., Halvorsen, P.: Commute path bandwidth traces from 3G networks: analysis and applications. In: ACM MMSys (2013)Google Scholar
  25. 25.
  26. 26.
    Spiteri, K., Urgaonkar, R., Sitaraman, R.K.: BOLA: near-optimal bitrate adaptation for online videos. In: IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, pp. 1–9, April 2016.  https://doi.org/10.1109/INFOCOM.2016.7524428
  27. 27.
    Spiteri, K., Sitaraman, R., Sparacio, D.: From theory to practice: Improving bitrate adaptation in the DASH reference player. In: ACM MMsys (2018)Google Scholar
  28. 28.
    Stohr, D., Frömmgen, A., Rizk, A., Zink, M., Steinmetz, R., Effelsberg, W.: Where are the sweet spots?: a systematic approach to reproducible DASH player comparisons. In: ACM Multimedia (2017)Google Scholar
  29. 29.
    Sun, Y., et al.: CS2P: improving video bitrate selection and adaptation with data-driven throughput prediction. In: ACM SIGCOMM (2016)Google Scholar
  30. 30.
    Timmerer, C., Maiero, M., Rainer, B.: Which Adaptation Logic? An Objective and Subjective Performance Evaluation of HTTP-based Adaptive Media Streaming Systems. CoRR (2016)Google Scholar
  31. 31.
    Wamser, F., Casas, P., Seufert, M., Moldovan, C., Tran-Gia, P., Hossfeld, T.: Modeling the YouTube stack: from packets to quality of experience. Comput. Netw. 109, 211–224 (2016)CrossRefGoogle Scholar
  32. 32.
    Wang, C., Rizk, A., Zink, M.: SQUAD: a spectrum-based quality adaptation for dynamic adaptive streaming over HTTP. In: ACM MMSys (2016)Google Scholar
  33. 33.
    Yan, F.Y., et al.: Learning in situ: a randomized experiment in video streaming. In: USENIX NSDI (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Melissa Licciardello
    • 1
  • Maximilian Grüner
    • 1
    Email author
  • Ankit Singla
    • 1
  1. 1.Department of Computer ScienceETH ZürichZürichSwitzerland

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