Two-Stage Hybrid Edge Caching Framework for 360° VR Video

The advent of virtual reality and immersive communication technologies is effecting a transformation in user experiences, enabling high-quality, low-latency interactions. In order to meet these requirements, edge caching, in particular for the transmission of virtual reality content, has become an efficient strategy for the mitigation of transmission latency and the decrease of backhaul traffic loads. This paper provides an introduction to a two-stage hybrid caching framework developed to manage the typical obstacles related to the caching of 360° video. The proposed framework comprises two stages: a learning stage, which employs a Deep Q-Network to predict cache replacement actions, and a solving stage, which utilizes Integer Linear Programming to refine and optimize caching decisions. Furthermore, an L2 edge cache architecture is designed with the goal of enhancing cache utilization and further alleviating backhaul traffic. Performance evaluations illustrate that the proposed framework significantly enhances the cache hit ratio and reduces latency and backhaul usage compared to other methods.