  The growing demand for virtual reality (VR) applications requires wireless
systems to provide a high transmission rate to support 360-degree video
streaming to multiple users simultaneously. In this paper, we propose an
intelligent reflecting surface (IRS)-aided rate-splitting (RS) VR streaming
system. In the proposed system, RS facilitates the exploitation of the shared
interests of the users in VR streaming, and IRS creates additional propagation
channels to support the transmission of high-resolution 360-degree videos. IRS
also enhances the capability to mitigate the performance bottleneck caused by
the requirement that all RS users have to be able to decode the common message.
We formulate an optimization problem for maximization of the achievable bitrate
of the 360-degree video subject to the quality-of-service (QoS) constraints of
the users. We propose a deep deterministic policy gradient with imitation
learning (Deep-GRAIL) algorithm, in which we leverage deep reinforcement
learning (DRL) and the hidden convexity of the formulated problem to optimize
the IRS phase shifts, RS parameters, beamforming vectors, and bitrate selection
of the 360-degree video tiles. We also propose RavNet, which is a deep neural
network customized for the policy learning in our Deep-GRAIL algorithm.
Performance evaluation based on a real-world VR streaming dataset shows that
the proposed IRS-aided RS VR streaming system outperforms several baseline
schemes in terms of system sum-rate, achievable bitrate of the 360-degree
videos, and online execution runtime. Our results also reveal the respective
performance gains obtained from RS and IRS for improving the QoS in multiuser
VR streaming systems.
