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This paper investigates the problem of providing ultra-reliable andenergy-efficient virtual reality (VR) experiences for wireless mobile users. Toensure reliable ultra-high-definition (UHD) video frame delivery to mobileusers and enhance their immersive visual experiences, a coordinated multipoint(CoMP) transmission technique and millimeter wave (mmWave) communications areexploited. Owing to user movement and time-varying wireless channels, thewireless VR experience enhancement problem is formulated as asequence-dependent and mixed-integer problem with a goal of maximizing users'feeling of presence (FoP) in the virtual world, subject to power consumptionconstraints on access points (APs) and users' head-mounted displays (HMDs). Theproblem, however, is hard to be directly solved due to the lack of users'accurate tracking information and the sequence-dependent and mixed-integercharacteristics. To overcome this challenge, we develop a parallel echo statenetwork (ESN) learning method to predict users' tracking information bytraining fresh and historical tracking samples separately collected by APs.With the learnt results, we propose a deep reinforcement learning (DRL) basedoptimization algorithm to solve the formulated problem. In this algorithm, weimplement deep neural networks (DNNs) as a scalable solution to produce integerdecision variables and solving a continuous power control problem to criticizethe integer decision variables. Finally, the performance of the proposedalgorithm is compared with various benchmark algorithms, and the impact ofdifferent design parameters is also discussed. Simulation results demonstratethat the proposed algorithm is more 4.14% energy-efficient than the benchmarkalgorithms.

Feeling of Presence Maximization: mmWave-Enabled Virtual Reality Meets Deep Reinforcement Learning