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

TL;DR

ECAS-ML leverages edge computing and machine learning to optimize adaptive streaming, significantly enhancing user experience by intelligently managing bitrate, stalls, and segment switches.

Contribution

This work introduces ECAS-ML, a novel edge-assisted adaptive streaming scheme that uses machine learning to improve QoE by better managing network variability.

Findings

ECAS-ML outperforms existing ABR algorithms in QoE metrics.

Machine learning effectively predicts optimal streaming parameters.

Edge computing enhances adaptive streaming performance.

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.