  Virtual reality (VR) is promising to fundamentally transform a broad spectrum
of industry sectors and the way humans interact with virtual content. However,
despite unprecedented progress, current networking and computing
infrastructures are incompetent to unlock VR's full potential. In this paper,
we consider delivering the wireless multi-tile VR video service over a mobile
edge computing (MEC) network. The primary goal is to minimize the system
latency/energy consumption and to arrive at a tradeoff thereof. To this end, we
first cast the time-varying view popularity as a model-free Markov chain to
effectively capture its dynamic characteristics. After jointly assessing the
caching and computing capacities on both the MEC server and the VR playback
device, a hybrid policy is then implemented to coordinate the dynamic caching
replacement and the deterministic offloading, so as to fully utilize the system
resources. The underlying multi-objective problem is reformulated as a
partially observable Markov decision process, and a deep deterministic policy
gradient algorithm is proposed to iteratively learn its solution, where a long
short-term memory neural network is embedded to continuously predict the
dynamics of the unobservable popularity. Simulation results demonstrate the
superiority of the proposed scheme in achieving a trade-off between the energy
efficiency and the latency reduction over the baseline methods.

Networking and Internet Architecture;Machine Learning