AI 中文总结
研究L4S对视频流量影响，用开源DualPI2在Mahimahi模拟器上评估，增强SCReAM BW工具生成视频流量，在多种条件下评估网络级和QoE指标，结果表明评估L4S时QoE与网络级指标都重要。
AI 中文摘要
对低延迟、低损耗和可扩展吞吐量（L4S）的兴趣日益增长，反映了交互式多媒体应用对更低延迟的需求。本文使用Mahimahi模拟器上的开源DualPI2实现来评估L4S对SCReAM拥塞控制视频流量的影响。为此，我们用视频编解码器增强了SCReAM BW工具，使其除了原始合成RTP模式外还能生成视频流量。我们在移动网络跟踪、随机丢包和不同运动复杂度水平下评估网络级和体验质量（QoE）指标。在基线场景中，L4S将每次运行的第95百分位数队列延迟中位数降低了35%，代价是发送方吞吐量下降42%。在1%丢包情况下，与无损场景相比，L4S带来更显著的QoE提升，尽管网络级收益更窄。在不同视频内容复杂度下，L4S也比传统方法保持更稳定的QoE。这些结果强调了在评估L4S对终端用户应用性能的影响时，同时评估QoE和网络级指标的重要性。
英文摘要
The growing interest in Low Latency, Low Loss, and Scalable Throughput (L4S) reflects the need for lower latency in interactive multimedia applications. In this paper, we use an open-source DualPI2 implementation over the Mahimahi emulator to evaluate the impact of L4S on SCReAM congestion controlled video traffic. To do so, we augment the SCReAM BW tool with a video codec, enabling the generation of video traffic in addition to its original synthetic RTP mode. We evaluate both network-level and Quality of Experience (QoE) metrics on a mobile network trace, under random packet loss, and with different motion-complexity levels. In our baseline scenario, L4S reduces the median per-run $95^{th}$ percentile queue delay by 35%, at the cost of a 42% drop in sender throughput. Under 1% packet loss, L4S yields more pronounced QoE gains compared to the lossless scenario, despite narrower network-level benefits. Across video content complexities, L4S also maintains more stable QoE than Classic. These results underscore the importance of evaluating QoE alongside network-level metrics when assessing the effect of L4S on end-user application performance.
