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.

翻译：基于5G的视频会议日益普及，但其体验质量（QoE）在有限无线资源下常出现下降。这源于两个原因：5G网络需服务大量用户，同时交互式流量需要精细处理。受交互会话中不同子流对QoE影响程度不均这一现象的启发，我们提出了StreamGuard的设计与实现——一种面向子流级、QoE感知优先级的实用5G架构。StreamGuard构建了一个包含三个组件的闭环控制回路：（1）无线接入网（RAN）中的监测器，通过深度包检测推断QoE与RAN状态；（2）控制器，选择优先级操作以平衡QoE与公平性；（3）标记模块，通过标记数据包将子流导向相应优先级队列来执行决策。StreamGuard进一步通过选择性子流丢弃和基于探测的速率控制等机制塑造应用行为，使应用行为与无线资源约束相适配。在真实5G测试平台上的实验表明，相较于原生5G及现有最优方法，StreamGuard实现了更优的QoE-公平性权衡：在同等后台吞吐量下将QoE提升高达70%，或在相似QoE下将后台吞吐量提升至2倍。