Improving Cloud Gaming Traffic QoS: A Comparison Between Class-Based Queuing Policy and L4S

Some kinds of application traffic, such as Cloud Gaming (CG), are particularly demanding for a network to transport because they require at the same time a low-latency and a high-bitrate. Quality of Experience (QoE) can quickly deteriorate when the network Quality of Service (QoS) is not met regarding bandwidth and delay requirements. In particular, the competition with some capacity seeking flows may induce a high queuing latency on the bottleneck (buffer-bloat phenomenon). In this paper, we evaluate two network level solutions that allow CG traffic to be processed in specific queues, but exhibit different operational constraints. The first solution uses a class-based queuing policy (Hierarchical Token Buckets, HTB), which requires prior traffic classification and some traffic engineering. The second solution leverages the new Low Latency, Low Loss & Scalable Throughput (L4S) architecture and the DualPI2 Active Queue Management (AQM), but it needs the application support. We perform extensive measurements on an experimental CG platform that integrates the L4S-compliant SCReAM CCA and that we made to evaluate both approaches regarding their QoS enforcement capability and fairness against different competing flows that are driven by TCP CUBIC or BBRv2. We show that both solutions succeed to preserve the QoS of CG traffic.