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Understanding Video Streaming Algorithms in the Wild

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Passive and Active Measurement (PAM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 12048))

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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.

M. Licciardello and M. Grüner—Equal contribution.

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Notes

  1. 1.

    At the bandwidth levels seen in our traces, bottlenecks are at our client—our university’s connectivity to large services is otherwise high-bandwidth, consistently resulting in the highest-quality playback available on each service.

  2. 2.

    To avoid the unintended use of our scripts for downloading copyright-protected content, we refrain from publishing code for this part of our pipeline.

  3. 3.

    Specifically, the stable collection from September 2017 [9].

  4. 4.

    Note that these inefficiencies cannot be blamed on transport/TCP alone, as on the same traces, other players are able to use \(80\%\) of the available capacity. We also carefully account for non-video data to ensure we are not simply ignoring non-chunk data in these calculations. For instance, audio data is separately delivered for Vimeo and YouTube, but is accounted for appropriately in our bandwidth use analysis.

  5. 5.

    This ABR estimates throughput, T, as the mean of the last 5 throughput measurements. For its next download, it then picks the highest quality level with a bitrate \(\le T\). It thus downloads the largest chunk for which the estimated download time does not exceed the playback time.

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Authors and Affiliations

  1. Department of Computer Science, ETH Zürich, Zürich, Switzerland

    Melissa Licciardello, Maximilian Grüner & Ankit Singla

Authors
  1. Melissa Licciardello
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  2. Maximilian Grüner
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  3. Ankit Singla
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Corresponding author

Correspondence to Maximilian Grüner .

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Editors and Affiliations

  1. University of Twente, Enschede, The Netherlands

    Anna Sperotto

  2. University of California, San Diego, CA, USA

    Alberto Dainotti

  3. University of Zürich, Zürich, Switzerland

    Burkhard Stiller

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Licciardello, M., Grüner, M., Singla, A. (2020). Understanding Video Streaming Algorithms in the Wild. In: Sperotto, A., Dainotti, A., Stiller, B. (eds) Passive and Active Measurement. PAM 2020. Lecture Notes in Computer Science(), vol 12048. Springer, Cham. https://doi.org/10.1007/978-3-030-44081-7_18

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  • DOI: https://doi.org/10.1007/978-3-030-44081-7_18

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  • Online ISBN: 978-3-030-44081-7

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