Prediction, Communication, and Computing Duration Optimization for VR Video Streaming

Abstract: Proactive tile-based video streaming can avoid motion-to-photon latency of wireless virtual reality (VR) by computing and delivering the predicted tiles to be requested before playback. All existing works either focus on designing predictors or allocating computing and communications resources. Yet to avoid the latency, the successively executed prediction, communication, and computing tasks should be accomplished within a predetermined time. Moreover, the quality of experience (QoE) of proactive VR streaming depends on the worst performance of the three tasks. In this paper, we jointly optimize the duration of the observation window for predicting tiles and the durations for computing and transmitting the predicted tiles, aimed at balancing the performance for three tasks to maximize the QoE given arbitrary predictor and configured resources. We obtain the closed-form optimal solution by decomposing the formulated problem equivalently into two subproblems. With the optimized durations, we find a resource-limited region where the QoE increases rapidly with configured resources, and a prediction-limited region where the QoE can be improved more efficiently with a better predictor. Simulation results using three existing predictors and a real dataset validate the analysis and demonstrate the gain from the joint optimization over non-optimized counterparts.