Motion-Prediction-Based Wireless Scheduling for Interactive Panoramic Scene Delivery.

Mobile Virtual Reality (VR) and panoramic video streaming rely on interactive panoramic scene delivery to provide desirable user experiences. However, it is pretty challenging to support multiple users via the wireless network since a panoramic scene typically consumes $4\sim 6\times$ bandwidth compared with a regular video with the same resolution. Motivated by the fact that users only perceive the Field-of-View (FoV), we employ the autoregressive process to predict the user's motion and stream only part of the panoramic content. Notably, we analytically characterize the effect of the delivered portion on the user's successful viewing probability. Then, we formulate an optimization problem to maximize the application-level throughput (which measures the average rate for successful viewing the desired content instead of raw network throughput) while providing a regular service. In addition, we impose three main constraints to our problem: minimum required service rate, maximum allowable energy consumption, and wireless interference. We then propose a novel scheduling algorithm that incorporates users' successful viewing probabilities and asymptotically maximizes application-level throughput while providing service regularity guarantees. We conduct real-trace simulations to evaluate the efficiency of our algorithm.