  In this paper, we present a novel content caching and delivery approach for
mobile virtual reality (VR) video streaming. The proposed approach aims to
maximize VR video streaming performance, i.e., minimizing video frame missing
rate, by proactively caching popular VR video chunks and adaptively scheduling
computing resources at an edge server based on user and network dynamics.
First, we design a scalable content placement scheme for deciding which video
chunks to cache at the edge server based on tradeoffs between computing and
caching resource consumption. Second, we propose a machine learning-assisted VR
video delivery scheme, which allocates computing resources at the edge server
to satisfy video delivery requests from multiple VR headsets. A Whittle
index-based method is adopted to reduce the video frame missing rate by
identifying network and user dynamics with low signaling overhead. Simulation
results demonstrate that the proposed approach can significantly improve VR
video streaming performance over conventional caching and computing resource
scheduling strategies.
