Value-Driven XR Transmission in Multi-Connectivity Assisted Communication System
Xiaoyu Chi, Shujun Han, Xiaodong Xu, Hui Wang, Ze Liu, Liang Jin, Ping Zhang, Tony Q. S. Quek · IEEE Transactions on Cognitive Communications and Networking · 2025
The International Telecommunication Union (ITU) has proposed immersive communication scenarios, such as Extended Reality (XR), as exemplary use cases for 6G networks, intensifying the concurrent demands for low latency and high throughput. Consequently, the design of 6G solutions should not only focus on how to further increase data transmission rates but also on how to transmit the most valuable data to utilize limited communication resources more efficiently, thereby achieving superior system performance. In this paper, we aim to enhance the XR user experience in terms of timeliness and resolution under resource constrained scenarios. On the one hand, we increase the wireless communication system transmission capability via multi-connectivity technology. On the other hand, we reduce the source transmitting data via transmitting valuable source data. Specifically, we design a value-driven multi-connectivity transmission scheme that jointly optimizes source decision and resource allocation for XR services, aiming to efficiently utilize the resources for serving XR services. Furthermore, we design the Value of Video Data (VoVD) as the metric to comprehensively measure the XR performance, which balances timeliness and resolution. We also propose a Cascaded Dimension-Refined Reinforcement Learning (CDRRL) algorithm, which delivers a stable, low-complexity solution. The numerical results confirm the significant performance improvements and algorithm stability.