Fake It No More: Evaluating L4S with SCReAM on Video Traffic

2026-07-26

AI Summary

This study evaluates the impact of Low Latency, Low Loss, and Scalable throughput (L4S) mechanisms on network performance and Quality of Experience (QoE) under real video traffic. Innovatively integrating actual video codecs into the SCReAM congestion control algorithm—replacing conventional synthetic RTP traffic—the work implements L4S using the Mahimahi network emulator combined with DualPI2. Comprehensive experiments analyze joint network and QoE metrics across diverse mobile network conditions, packet loss rates, and video content complexities. Results demonstrate that, in baseline scenarios, L4S reduces the 95th-percentile queueing delay by 35% (with a 42% throughput reduction) and significantly improves QoE under 1% packet loss. Furthermore, L4S consistently delivers superior QoE stability compared to traditional approaches across varying video content.

Abstract

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.

Problem

• L4S
• SCReAM
• video traffic
• QoE
• congestion control

Innovation

• L4S
• SCReAM
• QoE
• video traffic
• network emulation