StreamGuard: Exploring a 5G Architecture for Efficient, Quality of Experience-Aware Video Conferencing

2026-04-28

AI Summary

This work addresses the challenge of maintaining both multi-user fairness and real-time interactivity in 5G video conferencing under wireless resource constraints, which otherwise leads to significant degradation in Quality of Experience (QoE). The paper proposes the first substream-level QoE-aware architecture for 5G, featuring a closed-loop design composed of monitoring, control, and marking modules that dynamically adjust substream priorities. By synergistically integrating selective packet dropping and probe-based rate control, the framework jointly optimizes application and network behaviors. It achieves, for the first time in a 5G system, substream-granularity QoE-aware scheduling by incorporating key techniques such as deep packet inspection and RAN state awareness. Real-world experiments on a 5G testbed demonstrate up to a 70% QoE improvement over native 5G and state-of-the-art approaches, or alternatively, a 2× increase in background throughput at comparable QoE levels.

Abstract

Video conferencing over 5G is increasingly prevalent, yet its Quality of Experience (QoE) often degrades under limited radio resources. This has two causes: 5G networks must serve many users, while interactive traffic requires careful handling. Motivated by the insight that different subflows within an interactive session have a disproportionate effect on QoE, we present the design and implementation of StreamGuard, a practical 5G architecture for subflow-level, QoE-aware prioritization. StreamGuard forms a closed control loop with three components: (1) a monitor in the Radio Access Network (RAN) that uses deep packet inspection to infer QoE and RAN state, (2) a controller that selects prioritization actions to balance QoE and fairness, and (3) a marking module that applies these decisions by marking packets to steer subflows into appropriate priority queues. StreamGuard further shapes application behaviors via mechanisms including selective subflow dropping and probe-based rate control, to align application behavior with radio constraints. Implemented in a real 5G testbed, StreamGuard achieves a superior QoE-fairness tradeoff compared to vanilla 5G and prior state-of-the-art approaches, improving QoE by up to 70% at comparable background throughput or preserving up to 2x higher background throughput at similar QoE.

Problem

• 5G
• Quality of Experience
• video conferencing
• radio resource limitation
• interactive traffic

Innovation

• QoE-aware
• subflow-level prioritization
• 5G RAN
• closed-loop control
• video conferencing