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Proactive tile-based video streaming can avoid motion-to-photon latency ofwireless virtual reality (VR) by computing and delivering the predicted tilesto be requested before playback. All existing works either focus on the task oftile prediction or on the tasks of computing and communications, overlookingthe facts that these successively executed tasks have to share the sameduration to avoid the latency and the quality of experience (QoE) of proactiveVR streaming depends on the worst performance of the three tasks. In thispaper, we jointly optimize the duration of the observation window forpredicting tiles and the durations for computing and transmitting the predictedtiles to maximize the QoE given arbitrary predictor and configured resources.We obtain the global optimal solution with closed-form expression bydecomposing the formulated problem equivalently into two subproblems. With theoptimized durations, we find a resource-limited region where the QoE can beimproved effectively by configuring more resources, and a prediction-limitedregion where the QoE can be improved with a better predictor. Simulationresults using three existing tile predictors with a real dataset demonstratethe gain of the joint optimization over the non-optimized counterparts.

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