  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%.
