Leveraging 5G Physical Layer Monitoring for Adaptive Remote Rendering in XR Applications

TL;DR

This work tackles the challenge of maintaining high-quality remote XR experiences over fluctuating 5G networks by exploiting physical-layer monitoring to estimate available bandwidth. It introduces a bandwidth estimation approach grounded in 3GPP specifications and a maximum data-rate equation to derive real-time network capacity, which then drives adaptive streaming in XR. The authors implement a two-module architecture (Rate Controller and XR Remote Renderer) with predefined quality profiles and a stability constraint to adjust bitrate, framerate, and resolution. Experimental evaluation on a controlled Amarisoft 5G testbed demonstrates that the estimated data rate tracks measured throughput and that adaptive streaming can reduce total freeze time under mobility, highlighting the practical potential of incorporating physical-layer insights into XR QoS management. Overall, the work bridges 5G physical-layer information with XR rendering decisions to enhance QoS in dynamic network conditions.

Abstract

As immersive eXtended Reality (XR) applications demand substantial network resources, understanding their interaction with 5G networks becomes crucial to improve them. This paper investigates the role of 5G physical-layer monitoring to manage and enhance the remote rendering of XR content dynamically. By observing network metrics directly from the physical layer, we propose a system to adapt streaming parameters such as bitrate, framerate, and resolution in real time based on available network capacity. Using theoretical formulas to estimate maximum data rate, our approach evaluates network resource availability, enabling the renderer to self-adjust media content representation. This is critical for providing consistent and smooth XR experiences to users, especially as network conditions fluctuate. Our findings suggest that physical-layer monitoring offers valuable insights to increase the Quality of Service (QoS) and has the potential to elevate user experience in remote-rendered XR applications.

Figures (5)

• Figure 1: Architecture for auto-adaptive XR Remote Rendering.
• Figure 2: Sequence diagram.
• Figure 3: Comparison between estimated data rate and measured throughput over time.
• Figure 4: Estimated data rate and current streaming profile.
• Figure 5: Number of freezes and current streaming profile.