  Immersive virtual reality (VR) applications require ultra-high data rate and
low-latency for smooth operation. Hence in this paper, aiming to improve VR
experience in multi-user VR wireless video streaming, a deep-learning aided
scheme for maximizing the quality of the delivered video chunks with
low-latency is proposed. Therein the correlations in the predicted field of
view (FoV) and locations of viewers watching 360$^\circ$ HD VR videos are
capitalized on to realize a proactive FoV-centric millimeter wave (mmWave)
physical-layer multicast transmission. The problem is cast as a frame quality
maximization problem subject to tight latency constraints and network
stability. The problem is then decoupled into an HD frame request admission and
scheduling subproblems and a matching theory game is formulated to solve the
scheduling subproblem by associating requests from clusters of users to mmWave
small cell base stations (SBSs) for their unicast/multicast transmission.
Furthermore, for realistic modeling and simulation purposes, a real VR
head-tracking dataset and a deep recurrent neural network (DRNN) based on gated
recurrent units (GRUs) are leveraged. Extensive simulation results show how the
content-reuse for clusters of users with highly overlapping FoVs brought in by
multicasting reduces the VR frame delay in 12\%. This reduction is further
boosted by proactiveness that cuts by half the average delays of both reactive
unicast and multicast baselines while preserving HD delivery rates above 98\%.
Finally, enforcing tight latency bounds shortens the delay-tail as evinced by
13\% lower delays in the 99th percentile.
