  Virtual Reality (VR) applications require high data rate for a high-quality
immersive experience, in addition to low latency to avoid dizziness and motion
sickness. One of the key wireless VR challenges is providing seamless
connectivity and meeting the stringent latency and bandwidth requirements. This
work proposes a proactive wireless VR system that utilizes information about
the user's future orientation for proactive scheduling and caching. This is
achieved by leveraging deep neural networks to predict users' orientation
trained on a real dataset. The 360{\deg} scene is then partitioned using an
overlapping viewports scheme so that only portions of the scene covered by the
users' perceptive field-of-view are streamed. Furthermore, to minimize the
backhaul latency, popular viewports are cached at the edge cloud based on
spatial popularity profiles. Through extensive simulations, we show that the
proposed system provides significant latency and throughput performance
improvement, especially in fluctuating channels and heavy load conditions. The
proactive scheduling enabled by the combination of machine learning prediction
and the proposed viewport scheme reduces the mean latency by more than 80%
while achieving successful delivery rate close to 100%.
