Reinforcement Learning-Based Dynamic Resource Allocation for Aerial 360° Video VR Streaming

Efficient power use and accurate viewport information are key factors in enabling effective aerial 360° video delivery to virtual reality (VR) clients for emerging remote immersion societal applications. We explore a learning-based framework for transmission power allocation and robust viewport identification in UAV-based 360° video streaming to a ground user/VR client that aims to maximize the delivered viewport quality and minimize the video playback stall time on the user's VR headset. We model the problem of interest as a Markov decision process (MDP) encompassing the UAV's transmit power, the VR client's video playback stall time, and the full-identification outage of the user's observed viewport in the MDP reward function. Our framework integrates an effective scalable 360° video tiling representation of the captured content that ensures for the client (i) maximum delivered viewport quality given the available UAV transmission rate and (ii) VR application robustness to partial viewport outages, at the same time. We formulate a novel learning-based method for adaptive transmission power allocation and predicted viewport enlargement, to solve the problem of interest. Relative to multiple reference methods, we demonstrate through experiments that our framework can achieve up to 8dB improvement in viewport PSNR and an 85% reduction in full-viewport identification outage, while using 60% less transmit power and experiencing negligible video stall times.