  Massive multiple-input and multiple-output (MIMO) enables ultra-high
throughput and low latency for tile-based adaptive virtual reality (VR) 360
video transmission in wireless network. In this paper, we consider a massive
MIMO system where multiple users in a single-cell theater watch an identical VR
360 video. Based on tile prediction, base station (BS) deliveries the tiles in
predicted field of view (FoV) to users. By introducing practical supplementary
transmission for missing tiles and unacceptable VR sickness, we propose the
first stable transmission scheme for VR video. we formulate an integer
non-linear programming (INLP) problem to maximize users' average quality of
experience (QoE) score. Moreover, we derive the achievable spectral efficiency
(SE) expression of predictive tile groups and the approximately achievable SE
expression of missing tile groups, respectively. Analytically, the overall
throughput is related to the number of tile groups and the length of pilot
sequences. By exploiting the relationship between the structure of viewport
tiles and SE expression, we propose a multi-lattice multi-stream grouping
method aimed at improving the overall throughput for VR video transmission.
Moreover, we analyze the relationship between QoE objective and number of
predictive tile. We transform the original INLP problem into an integer linear
programming problem by setting the predictive tiles groups as some constants.
With variable relaxation and recovery, we obtain the optimal average QoE.
Extensive simulation results validate that the proposed algorithm effectively
improves QoE.
