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 5.9% to 80.0%.
Analysis and Expert Insights
This article highlights the significance of Extended Reality (XR) services in the context of 5G and future 6G networks. XR encompasses a wide range of technologies including Virtual Reality (VR), Augmented Reality (AR), and Artificial Reality (AR) that provide immersive and interactive experiences to users. As XR video transmission is highly sensitive to network fluctuations and requires low latency, ensuring a high Quality of Experience (QoE) becomes crucial.
The paper introduces a QoE model for real-time XR video transmission on a frame-by-frame basis. By modeling the QoE, the authors aim to optimize wireless resources and long-term energy consumption while maximizing user satisfaction. They employ Lyapunov optimization techniques to transform the problem into a single-frame optimization problem, allowing for efficient allocation of wireless subchannels.
To further enhance the performance of XR video transmission, the authors propose an adaptive XR video bitrate algorithm that utilizes a Long Short Term Memory (LSTM) based Deep Q-Network (DQN). This algorithm dynamically selects the video bitrate based on the current network conditions, ensuring optimal video quality and reducing the impact of network fluctuations on user experience.
The results of their numerical experiments demonstrate the superiority of their proposed algorithm over baseline algorithms. The average QoE improvements ranging from 5.9% to 80.0% indicate the effectiveness of their approach in enhancing user satisfaction during real-time XR video transmission.
Overall, this research contributes to the wider field of multimedia information systems by addressing the unique challenges posed by real-time XR video transmission in 5G and future 6G networks. The multi-disciplinary nature of the concepts discussed, including wireless communication, optimization theory, deep learning, and human-computer interaction, showcases the complexity of developing advanced XR services. By leveraging cutting-edge techniques such as Lyapunov optimization and LSTM-based DQN, this paper provides valuable insights into improving QoE and optimizing resource allocation in XR video transmission.
Read the original article