ECAS-ML: Edge Computing Assisted Adaptation Scheme with Machine Learning for HTTP Adaptive Streaming
This work addresses video streaming efficiency for mobile network users, representing an incremental improvement by integrating edge computing with machine learning for adaptive bitrate algorithms.
The paper tackles the problem of improving video streaming quality in mobile networks by proposing ECAS-ML, an edge computing assisted adaptation scheme that uses machine learning to optimize bitrate, segment switches, and stalls, resulting in higher quality of experience and outperforming other ABR algorithms.
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.