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Evaluating Adaptive Video Streaming over Multipath QUIC with Shared Bottleneck Detection
Federal University of São Paulo, Brazil; King’s College London, UK.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). Red Hat, Sweden.ORCID iD: 0000-0002-0722-2656
CPqD R&D Centre for Telecommunications; Federal University of São Paulo, Brazil.
King's Coll London, England..
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2025 (English)In: ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP), ISSN 1551-6857, E-ISSN 1551-6865, Vol. 21, no 9, article id 246Article in journal (Refereed) Published
Abstract [en]

The promises of multipath transport are to aggregate bandwidth, improve resource utilisation and enhance reliability. In this article, we demonstrate that the way multipath coupled congestion control is defined today leads to a suboptimal resource utilisation when network paths are disjoint, i.e., they do not share a bottleneck link. With growing interest in standardising Multipath QUIC (MPQUIC), we have implemented the practical experiments, we evaluate MPQUIC-SBD in the context of video streaming with various Adaptive Bitrate (ABR) algorithms, addressing both ABR classes of rule- and learning-based solutions. We demonstrate that MPQUIC-SBD accurately detects shared bottlenecks over 90% of the time, depending on the ABR algorithm, as the size of the video segments increases. In non-shared bottleneck scenarios, when MPQUIC-SBD detects that its QUIC subflows do not share the same network resources, it decouples their congestion windows accordingly, enabling video throughput gains of up to 37% compared to MPQUIC. These gains translate directly into improved video quality metrics, including higher bitrate, better resolution and reduced buffering, resulting in an enhanced quality of experience for users.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2025. Vol. 21, no 9, article id 246
Keywords [en]
DASH, Rule-based ABR, Learning-based ABR, QUIC, Multipath QUIC, Shared Bottleneck Detection, Congestion Control, Multipath Transport
National Category
Computer Sciences Telecommunications
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-107334DOI: 10.1145/3711862ISI: 001580782200004Scopus ID: 2-s2.0-105018669326OAI: oai:DiVA.org:kau-107334DiVA, id: diva2:2007505
Available from: 2025-10-20 Created: 2025-10-20 Last updated: 2026-02-12Bibliographically approved

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Ferlin, SimoneBrunstrom, AnnaAlay, Özgü

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