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：通过5G进行视频会议日益普及，但在有限的无线资源下，其体验质量（QoE）常会下降。导致该问题的原因有两个：5G网络需服务大量用户，而交互式流量需要精细处理。基于交互会话中不同子流对QoE影响存在非对称效应的洞察，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构成一个闭环控制回路，包含三个组件：（1）无线接入网（RAN）中的监控器，通过深度包检测推断QoE与RAN状态；（2）控制器，选择优先级动作以平衡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上述决策。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调整应用行为，使其与无线资源限制相匹配。在真实5G测试平台上的实现表明，与原始5G及现有最优方法相比，StreamGuard实现了更优的QoE-公平性权衡：在可比背景吞吐量下提升QoE高达70%，或在相似QoE水平下保持高达2倍的背景吞吐量。