  Extended reality (XR) is one of the most important applications of beyond 5G
and 6G networks. Real-time XR video transmission presents challenges in terms
of data rate and delay. In particular, the frame-by-frame transmission mode of
XR video makes real-time XR video very sensitive to dynamic network
environments. To improve the users' quality of experience (QoE), we design a
cross-layer transmission framework for real-time XR video. The proposed
framework allows the simple information exchange between the base station (BS)
and the XR server, which assists in adaptive bitrate and wireless resource
scheduling. We utilize the cross-layer information to formulate the problem of
maximizing user QoE by finding the optimal scheduling and bitrate adjustment
strategies. To address the issue of mismatched time scales between two
strategies, we decouple the original problem and solve them individually using
a multi-agent-based approach. Specifically, we propose the multi-step Deep
Q-network (MS-DQN) algorithm to obtain a frame-priority-based wireless resource
scheduling strategy and then propose the Transformer-based Proximal Policy
Optimization (TPPO) algorithm for video bitrate adaptation. The experimental
results show that the TPPO+MS-DQN algorithm proposed in this study can improve
the QoE by 3.6% to 37.8%. More specifically, the proposed MS-DQN algorithm
enhances the transmission quality by 49.9%-80.2%.
