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ECAS-ML: Edge Computing Assisted Adaptation Scheme with Machine Learning for HTTP Adaptive Streaming

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MultiMedia Modeling (MMM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13142))

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Abstract

As the video streaming traffic in mobile networks is increasing, improving the content delivery process becomes crucial, e.g., by utilizing edge computing support. At an edge node, we can deploy adaptive bitrate (ABR) algorithms with a better understanding of network behavior and access to radio and player metrics. In this work, we present ECAS-ML, Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming with Machine Learning. ECAS-ML focuses on managing the tradeoff among bitrate, segment switches and stalls to achieve a higher quality of experience (QoE). For that purpose, we use machine learning techniques to analyze radio throughput traces and predict the best parameters of our algorithm to achieve better performance. The results show that ECAS-ML outperforms other client-based and edge-based ABR algorithms.

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Notes

  1. 1.

    http://ftp.itec.aau.at/datasets/DASHDataset2014/BigBuckBunny/2sec/.

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Authors and Affiliations

  1. Christian Doppler Laboratory ATHENA, Institute of Information Technology, Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria

    Jesús Aguilar-Armijo, Ekrem Çetinkaya, Christian Timmerer & Hermann Hellwagner

Authors
  1. Jesús Aguilar-Armijo
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  2. Ekrem Çetinkaya
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  3. Christian Timmerer
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  4. Hermann Hellwagner
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Corresponding author

Correspondence to Jesús Aguilar-Armijo .

Editor information

Editors and Affiliations

  1. IT University of Copenhagen, Copenhagen, Denmark

    Björn Þór Jónsson

  2. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  3. University of Science, VNU-HCM, Ho Chi Minh City, Vietnam

    Minh-Triet Tran

  4. University of Bergen, Bergen, Norway

    Duc-Tien Dang-Nguyen

  5. National Tsing Hua University, Hsinchu, Taiwan

    Anita Min-Chun Hu

  6. Hanoi University of Science and Technology, Hanoi, Vietnam

    Binh Huynh Thi Thanh

  7. Median Technologies, Valbonne, France

    Benoit Huet

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Aguilar-Armijo, J., Çetinkaya, E., Timmerer, C., Hellwagner, H. (2022). ECAS-ML: Edge Computing Assisted Adaptation Scheme with Machine Learning for HTTP Adaptive Streaming. In: Þór Jónsson, B., et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13142. Springer, Cham. https://doi.org/10.1007/978-3-030-98355-0_33

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  • DOI: https://doi.org/10.1007/978-3-030-98355-0_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98354-3

  • Online ISBN: 978-3-030-98355-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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