Multi-Access Edge Computing Enhanced Video Streaming: Proof-of-Concept Implementation and Prediction/QoE Models

Abstract

ETSI multi-access edge computing (MEC) provides an IT service environment and cloud-computing capabilities at the edge of the mobile network, enabling application and content providers to deploy new use cases, such as intelligent video acceleration, with low latency and high bandwidth. Specifically, ETSI MEC introduces an MEC server that implements the edge-cloud platform to host partial server-side service logics in the form of MEC applications (MEC Apps). In this paper, we aim to implement the first proof-of-concept (PoC) in the literature for the MEC-enhanced mobile video streaming service. Our PoC consists of Android User Apps, an MEC App, and the YouTube server. The MEC App implements two main functions: popular video caching and radio analytics/video quality adaptation. The User App provides general functions of a YouTube video streaming app and can access the videos from the cache server or the YouTube server under the MEC server's guidance. In addition to the PoC implementation, this paper further develops two machine learning models to be incorporated into the MEC App for popular video prediction and radio channel quality prediction, which allows to consider the effect of non-negligible round-trip times and adjust the video quality more accurately. The experimental results justify that our models, together with other advantages from MEC, can guarantee good performance for the mobile video streaming service. Finally, we model and investigate the effectiveness of the MEC architecture for improving the quality of experience of video-streaming users.