UX-aware Rate Allocation for Real-Time Media

TL;DR

This work addresses the gap between KPI-centric network QoS and user-experienced UX for immersive XR traffic by introducing a UX-aware resource allocation framework. It relies on edge collaboration where application servers share real-time media complexity via QB curves $Q_n(\cdot)$ and the network allocates bitrates to optimize a UX-based utility, enabling either QoE-capacity maximization or QoE fairness (MaxCap/MaxMin). The approach demonstrates substantial gains in UX capacity (up to 50%–100% over baselines) and reductions in bitrate and latency, validating a shift from pure QoS to QoE-oriented networking for XR. The findings suggest practical benefits for 6G systems, highlighting the value of application awareness, edge deployment, and fast feedback loops in meeting tight XR requirements.

Abstract

Immersive communications is a key use case for 6G where applications require reliable latency-bound media traffic at a certain data rate to deliver an acceptable User Experience (UX) or Quality-of-Experience (QoE). The Quality-of-Service (QoS) framework of current cellular systems (4G and 5G) and prevalent network congestion control algorithms for latency-bound traffic like L4S typically target network-related Key Performance Indicators (KPIs) such as data rates and latencies. Network capacity is based on the number of users that attain these KPIs. However, the UX of an immersive application for a given data rate and latency is not the same across users, since it depends on other factors such as the complexity of the media being transmitted and the encoder format. This implies that guarantees on network KPIs do not necessarily translate to guarantees on the UX. In this paper, we propose a framework in which the communication network can provide guarantees on the UX. The framework requires application servers to share real-time information on UX dependency on data rate to the network, which in turn, uses this information to maximize a UX-based network utility function. Our framework is motivated by the recent industry trends of increasing application awareness at the network, and pushing application servers towards the edge, allowing for tighter coordination between the servers and the 6G system. Our simulation results show that the proposed framework substantially improves the UX capacity of the network, which is the number of users above a certain UX threshold, compared to conventional rate control algorithms.

Figures (8)

• Figure 1: Scenario of interest: (a) Two UEs with similar channel conditions will get similar network resources, despite their different video complexities. (b) A UX-aware rate allocation can improve the network's performance.
• Figure 2: Snapshots of different scenes of a cloud game and their RD (PSNR) curves. Scene 1 requires a bitrate of $\sim 19$ Mbps to achieve a PSNR of 35 dB, while Scene 2 requires only $\sim 3$ Mbps to achieve the same PSNR value.
• Figure 3: Proposed framework to UX-aware rate allocation.
• Figure 4: (a) Ratio of satisfied UEs as a function of the number of UEs per cell. (b) Network QoE capacity as a function of the target QoE threshold $\gamma$.
• Figure 5: (a) Average source bitrate and (b) $99^\text{th}$ percentile frame delay, as a function of the number of UEs per cell.