Cache-Enabled Adaptive Video Streaming Over Vehicular Networks: A Dynamic Approach

Adaptive bitrate (ABR) streaming has recently been deployed in vehicular networks (VNs) to deal with the time-varying channels due to reasons such as high user mobility. Caching at the wireless edge (e.g., base station) to support ABR streaming is a challenging problem. In this paper, we propose a two time-scale dynamic caching scheme for ABR streaming in VNs, in which the video quality adaptation at the application layer and cache placement at the BS are performed at a larger time-scale while the video data transmission at the physical layer is performed at a smaller time-scale. Lyapunov optimization technique is employed to maximize the time-averaged network reward, which is the weighted sum of video quality and backhaul saving. Without the prior knowledge of channel statistics, we develop a dynamic cache algorithm (DCA) to obtain the video quality adaptation, cache placement, and radio bandwidth allocation decisions. For the arbitrary sample path of channel states, we compare the network reward achieved by DCA with that achieved by an optimal $T$ -slot lookahead algorithm, i.e., the knowledge of the future channel path over an interval of length $T$ time slots. Simulation results demonstrate the advantages of DCA for ABR streaming in time-varying VNs over the static cache approach.