28th International Conference on Multimedia Modeling (MMM)
April 05-08, 2022 | Qui Nhon, Vietnam
Jesús Aguilar Armijo (Alpen-Adria-Universität Klagenfurt), Ekrem Çetinkaya (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt) and Hermann Hellwagner (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, Machine Learning.