Cross-Layer Dynamics in Live Low-Latency: A Dataset of ABR, CC, and AQM Interactions

18th International Conference on Quality of Multimedia Experience

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Md Tariqul Islam (UNICAMP, Brazil),  Farzad Tashtarian (AAU, Austria),  Christian Esteve Rothenberg (UNICAMP, Brazil), Christian Timmerer (AAU, Austria).

Low-latency video streaming, such as Low-Latency DASH (LL-DASH), requires maintaining high Quality of Experience (QoE) under varying network conditions. In LL-DASH, QoE is jointly influenced not only by Adaptive Bitrate (ABR) decisions, but also by transport-layer Congestion Control (CC) and network-layer Active Queue Management (AQM), whose interactions remain insufficiently characterized due to limited cross-layer experimentation. Therefore, we present a large-scale LL-DASH dataset comprising approximately 2,000 controlled sessions across three dash.js ABR algorithms (L2A, Dynamic, LoLP), three CC schemes (CUBIC, BBRv1, Prague) across both TCP and QUIC transport protocols, four AQM configurations (FIFO, FQ-CoDel, CAKE, DualPI2), and multiple congestion scenarios. The dataset supports QoE-aware cross-layer analysis and ABR benchmarking under diverse network configurations and is available at: https://github.com/cd-athena/ ll-dash-crosslayer-dataset