ECAS-ML: Edge Computing Assisted Adaptation Scheme with ML for HAS

Published In: 28th International Conference on MultiMedia Modeling (MMM), June 06-10, 2022, Phu Quoc, Vietnam - PaperLink

Authors: Jesús Aguilar Armijo, Ekrem Çetinkaya, Christian Timmerer, and Hermann Hellwagner (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)

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.

Keywords: HTTP Adaptive Streaming, Edge Computing, Content Delivery, Network-Assisted Video Streaming, Quality Of Experience

@inproceedings{ecasml,
     author={Aguilar-Armijo, Jes{\'u}s and \c{C}etinkaya, Ekrem and Timmerer, Christian and Hellwagner, Hermann},
     title={ECAS-ML: Edge Computing Assisted Adaptation Scheme with Machine Learning for HTTP Adaptive Streaming},
     booktitle={MultiMedia Modeling},
     doi = {10.1007/978-3-030-98355-0_33},
     publisher = {Springer International Publishing},
     series = {MMM'22},
     year = {2022}
     pages={394--406}
}