RAN-aware adaptive video caching in multi-access edge computing networks

Abstract

Videos are expected to be a primary contributor to an anticipated massive surge in mobile network data. Caching the videos within the mobile network can significantly reduce the network load and Operational Expenditure (OPEX) for mobile network operators. Multi-access Edge Computing (MEC) can enable the video caching by providing processing and storage capabilities within the network. However, content providers usually employ Dynamic Adaptive Streaming over HTTP (DASH) for video streaming, which contains multiple bit-rate representations of videos. Constrained by its capacity, MEC can not cache all representations of popular videos. Video transcoding mitigates this issue to a certain extent by converting the higher available video bit-rate to a requested lower one; but, it can quickly exhaust the available edge processing power by transcoding a large number of videos in parallel. Therefore, caching appropriate video bit-rates that can serve the maximum number of users in the network is a non-trivial problem. To resolve this problem and to efficiently utilize the resources (processing and storage) at the network edge, we took a non-traditional approach for video caching that utilizes the network information provided by MEC's Radio Network Information (RNI) Application Program Interface (API). In particular, RNI API provides Radio Access Network (RAN) status information that can be employed to estimate the probability distribution of requested video qualities. In this work, we formulate the video caching problem as an Integer Linear Programming (ILP) for the hit-rate maximization. Since the optimization problem requires the knowledge of all future requests, it obviously cannot be used in real-time. Therefore, we develop a RAN-aware Adaptive VidEo cachiNg (RAVEN) method that uses network information to make an informed decision for video bit-rate selection in video caching coupled with transcoding and maximizes the number of served users form the network edge. Simulation results demonstrate that the RAVEN significantly outperforms state-of-the-art algorithms in the domain and performs closer to the optimal solution.