  This paper investigates adaptive streaming of one or multiple tiled 360
videos from a multi-antenna base station (BS) to one or multiple single-antenna
users, respectively, in a multi-carrier wireless system. We aim to maximize the
video quality while keeping rebuffering time small via encoding rate adaptation
at each group of pictures (GOP) and transmission adaptation at each
(transmission) slot. To capture the impact of field-of-view (FoV) prediction,
we consider three cases of FoV viewing probability distributions, i.e.,
perfect, imperfect, and unknown FoV viewing probability distributions, and use
the average total utility, worst average total utility, and worst total utility
as the respective performance metrics. In the single-user scenario, we optimize
the encoding rates of the tiles, encoding rates of the FoVs, and transmission
beamforming vectors for all subcarriers to maximize the total utility in each
case. In the multi-user scenario, we adopt rate splitting with successive
decoding and optimize the encoding rates of the tiles, encoding rates of the
FoVs, rates of the common and private messages, and transmission beamforming
vectors for all subcarriers to maximize the total utility in each case. Then,
we separate the challenging optimization problem into multiple tractable
problems in each scenario. In the single-user scenario, we obtain a globally
optimal solution of each problem using transformation techniques and the
Karush-Kuhn-Tucker (KKT) conditions. In the multi-user scenario, we obtain a
KKT point of each problem using the concave-convex procedure (CCCP). Finally,
numerical results demonstrate that the proposed solutions achieve notable gains
over existing schemes in all three cases. To the best of our knowledge, this is
the first work revealing the impact of FoV prediction on the performance of
adaptive streaming of tiled 360 videos.
