Prediction, Communication, and Computing Duration Optimization for VR Video Streaming
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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 the task of tile prediction or on the tasks of computing and communications, overlooking the facts that these successively executed tasks have to share the same duration to avoid the latency and 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 to maximize the QoE given arbitrary predictor and configured resources. We obtain the global optimal solution with closed-form expression by decomposing the formulated problem equivalently into two subproblems. With the optimized durations, we find a resource-limited region where the QoE can be improved effectively by configuring more resources, and a prediction-limited region where the QoE can be improved with a better predictor. Simulation results using three existing tile predictors with a real dataset demonstrate the gain of the joint optimization over the non-optimized counterparts.
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