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Adaptation logic for HTTP dynamic adaptive streaming using geo-predictive crowdsourcing for mobile users

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Abstract

The increasing demand for video streaming services with a high Quality of Experience (QoE) has prompted considerable research on client-side adaptation logic approaches. However, most algorithms use the client’s previous download experience and do not use a crowd knowledge database generated by users of a professional service. We propose a new crowd algorithm that maximizes the QoE. We evaluate our algorithm against state-of-the-art algorithms on large, real-life, crowdsourcing datasets. There are six datasets, each of which contains samples of a single operator (T-Mobile, AT&T or Verizon) from a single road (I100 or I405). All measurements were from Android cellphones. The datasets were provided by WeFi LTD and are public for academic users. Our new algorithm outperforms all other methods in terms of QoE (eMOS).

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Authors and Affiliations

  1. Communication Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel

    Ran Dubin, Ofer Hadar, Itay Katz & Ori Mashiach

  2. Center for Cyber Technology, Department of Computer Science, Ariel University, Ariel, Israel

    Amit Dvir & Ofir Pele

  3. Center for Cyber Technology, Department of Electrical and Electronics Engineering, Ariel University, Ariel, Israel

    Ofir Pele

Authors
  1. Ran Dubin
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  2. Amit Dvir
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  3. Ofir Pele
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  4. Ofer Hadar
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  5. Itay Katz
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  6. Ori Mashiach
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Corresponding author

Correspondence to Amit Dvir.

Additional information

Communicated by M. Claypool.

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Dubin, R., Dvir, A., Pele, O. et al. Adaptation logic for HTTP dynamic adaptive streaming using geo-predictive crowdsourcing for mobile users. Multimedia Systems 24, 19–31 (2018). https://doi.org/10.1007/s00530-016-0525-6

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  • DOI: https://doi.org/10.1007/s00530-016-0525-6

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