Towards network-wide QoE fairness using openflow-assisted adaptive video streaming
Open accessAbstract
Video streaming is an increasingly popular way to consume media content. Adaptive video streaming is an emerging delivery technology which aims to increase user QoE and maximise connection utilisation. Many implementations naively estimate bandwidth from a one-sided client perspective, without taking into account other devices in the network. This behaviour results in unfairness and could potentially lower QoE for all clients. We propose an OpenFlow-assisted QoE Fairness Framework that aims to fairly maximise the QoE of multiple competing clients in a shared network environment. By leveraging a Software Defined Networking technology, such as OpenFlow, we provide a control plane that orchestrates this functionality. The evaluation of our approach in a home networking scenario introduces user-level fairness and network stability, and illustrates the optimisation of QoE across multiple devices in a network.
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With SAND, bandwidth reservation and bitrate guidance strategies have attracted much attention in the multimedia streaming community.
“With SAND, bandwidth reservation and bitrate guidance strategies have attracted much attention in the multimedia streaming community [7], [107] [198], [196].”
From QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey (2020) · cites this paper as [7]
Some approaches, for example in [7,198], employ an optimization function that interacts with the controller to dynamically optimize QoE fairness of multiple competing clients by setting the bitrate for each streaming video in the network.
“Some of the approaches, for example in [7,198], employ an optimization function that interacts with the controller to dynamically optimize QoE fairness of multiple competing clients by setting the bitrate for each streaming video in the network.”
From QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey (2020) · cites this paper as [7]
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- Authors
- Georgopoulos, P., Elkhatib, Y., Broadbent, M., Mu, M., & Race, N.
- Affiliations
- Lancaster University (GB)
- Venue
- Proceedings of the 2013 ACM SIGCOMM workshop on Future human-centric multimedia networking
- Publisher
- ACM
- Published
- 2013-08-16
- Pages
- 15-20
- OpenAlex
- W2146232773
- Cited by
- 242
- References
- 23
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 6 · 2022: 8 · 2021: 12 · 2020: 18 · 2019: 30 · 2018: 37 · 2017: 43 · 2016: 35 · 2015: 33 · 2014: 13 · 2013: 1
- FWCI
- 20.95
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Lancaster EPrints (Lancaster University) (submittedVersion)
- Research Output (Edinburgh Napier University) (submittedVersion)
- Edinburgh Napier Research Repository (Edinburgh Napier University) (submittedVersion)
- Funders
- European Commission (258365, 318343)
- Natural Environment Research Council
- FP7 Information and Communication Technologies
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- adaptive video streaming, OpenFlow, software-defined networking, bandwidth estimation, home network, control plane, network stability
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