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Virtual Reality (VR) applications require high data rate for a high-qualityimmersive experience, in addition to low latency to avoid dizziness and motionsickness. One of the key wireless VR challenges is providing seamlessconnectivity and meeting the stringent latency and bandwidth requirements. Thiswork proposes a proactive wireless VR system that utilizes information aboutthe user's future orientation for proactive scheduling and caching. This isachieved by leveraging deep neural networks to predict users' orientationtrained on a real dataset. The 360{\deg} scene is then partitioned using anoverlapping viewports scheme so that only portions of the scene covered by theusers' perceptive field-of-view are streamed. Furthermore, to minimize thebackhaul latency, popular viewports are cached at the edge cloud based onspatial popularity profiles. Through extensive simulations, we show that theproposed system provides significant latency and throughput performanceimprovement, especially in fluctuating channels and heavy load conditions. Theproactive scheduling enabled by the combination of machine learning predictionand the proposed viewport scheme reduces the mean latency by more than 80%while achieving successful delivery rate close to 100%.

Proactive Scheduling and Caching for Wireless VR Viewport Streaming