  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.
