Online resource-aware video content recommendation in edge-caches for mobile users

Abstract

The coupling of content caching at the wireless network edge and video streaming recommendation systems has been thoroughly investigated to enhance the cache hit and improve the user quality of experience (QoE). However, the existing literature lacks studies addressing the joint problem of QoE and cache hit ratio maximization while considering device characteristics and dynamic network resources of mobile users. This study introduces On-RAViR, an online framework comprising a Channel Quality Indicator (CQI) prediction module and a heuristic algorithm. This framework aims to maximize both cache hit ratio and user QoE under two constraints: the quality of the user equipment (UE) wireless link and the computing capabilities of the UE. We evaluate our framework employing a real-world video content dataset and a third-party 5G trace dataset. The results demonstrate that our framework produces rapid and high-quality solutions, increasing user QoE by 20% on average when compared to a state-of-the-art caching and recommendation heuristic unaware of computing and network resources.