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%.

翻译：扩展现实（XR）是超越5G和6G网络最重要的应用之一。实时XR视频传输在数据速率和时延方面面临挑战。特别是，XR视频逐帧传输模式使得实时XR视频对动态网络环境非常敏感。为提升用户体验质量（QoE），我们设计了一种面向实时XR视频的跨层传输框架。所提框架允许基站（BS）与XR服务器之间的简单信息交互，从而支持自适应码率和无线资源调度。我们利用跨层信息，通过寻求最优调度和码率调整策略来构建最大化用户QoE的优化问题。针对两种策略时间尺度不匹配的问题，我们将原问题解耦并采用基于多智能体的方法分别求解。具体而言，我们提出多步深度Q网络（MS-DQN）算法以获得基于帧优先级的无线资源调度策略，进而提出基于Transformer的近似策略优化（TPPO）算法用于视频码率自适应。实验结果表明，本研究所提出的TPPO+MS-DQN算法可使QoE提升3.6%至37.8%。更具体而言，所提出的MS-DQN算法使传输质量提升49.9%-80.2%。