Extended Reality (XR) is an important service in the 5G network and in future 6G networks. In contrast to traditional video on demand services, real-time XR video is transmitted frame-by-frame, requiring low latency and being highly sensitive to network fluctuations. In this paper, we model the quality of experience (QoE) for real-time XR video transmission on a frame-by-frame basis. Based on the proposed QoE model, we formulate an optimization problem that maximizes QoE with constraints on wireless resources and long-term energy consumption. We utilize Lyapunov optimization to transform the original problem into a single-frame optimization problem and then allocate wireless subchannels. We propose an adaptive XR video bitrate algorithm that employs a Long Short Term Memory (LSTM) based Deep Q-Network (DQN) algorithm for video bitrate selection. Through numerical results, we show that our proposed algorithm outperforms the baseline algorithms, with the average QoE improvements of 0.04 to 0.46. Specifically, compared to baseline algorithms, the proposed algorithm reduces average video quality variations by 29% to 50% and improves the frame transmission success rate by 5% to 48%.

翻译：扩展现实（XR）是5G网络及未来6G网络中的一项重要服务。与传统视频点播服务不同，实时XR视频以逐帧方式传输，要求低延迟且对网络波动高度敏感。本文基于逐帧传输机制，对实时XR视频传输的体验质量（QoE）进行建模。在所提出的QoE模型基础上，我们构建了一个在无线资源和长期能耗约束下最大化QoE的优化问题。利用李雅普诺夫优化将原问题转化为单帧优化问题，并据此分配无线子信道。我们提出一种自适应XR视频比特率算法，该算法采用基于长短期记忆网络（LSTM）的深度Q网络（DQN）进行视频比特率选择。数值结果表明，所提算法优于基线算法，平均QoE提升0.04至0.46。具体而言，与基线算法相比，该算法将视频质量波动平均降低29%至50%，并将帧传输成功率提升5%至48%。