The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
McKeown (2008) · doi:10.1145/1355734.1355746 · Cited by 8,513
Video quality assessment based on structural distortion measurement
Wang, Z., Lu, L., & Bovik, A. C. (2004) · Signal Processing: Image Communication · doi:10.1016/s0923-5965(03)00076-6 · Cited by 1,024
Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE
Jiang, J., Sekar, V., & Zhang, H. (2012) · ACM International Conference on emerging Networking EXperiments and Technologies (CoNEXT) · doi:10.1145/2413176.2413189 · Cited by 694
Understanding the impact of video quality on user engagement
Dobrian, F., Sekar, V., Awan, A., Stoica, I. G., Joseph, D. A., Ganjam, A., Zhan, J., & Zhang, H. (2011) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/2018436.2018478 · Cited by 674
A tree-search algorithm for mixed integer programming problems
Dakin, R. J. (1965) · The Computer Journal · doi:10.1093/comjnl/8.3.250 · Cited by 641
An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP
Akhshabi, S., Begen, A. C., & Dovrolis, C. (2011) · doi:10.1145/1943552.1943574 · Cited by 616
Bufferbloat: Dark Buffers in the Internet
Gettys, J. (2011) · IEEE Internet Computing · doi:10.1109/mic.2011.56 · Cited by 600
Huang, T.-Y., Handigol, N., Heller, B., McKeown, N., & Johari, R. (2012) · ACM Internet Measurement Conference (IMC) · doi:10.1145/2398776.2398800 · Cited by 344
What happens when HTTP adaptive streaming players compete for bandwidth?
Akhshabi, S., Anantakrishnan, L., Begen, A. C., & Dovrolis, C. (2012) · doi:10.1145/2229087.2229092 · Cited by 343
Understanding the impact of video quality on user engagement
Dobrian, F., Sekar, V., Awan, A., Stoica, I. G., Joseph, D. A., Ganjam, A., Zhan, J., & Zhang, H. (2011) · ACM SIGCOMM Computer Communication Review · doi:10.1145/2043164.2018478 · Cited by 332
Research works printed but not linked: 1
- Mu M., 2013, LNCS, 302
Cited by
Sorted by citations
Items 201–242 of 242
Slicing Wi‐Fi links based on QoE video streaming fairness
de Oliveira, M. C., & Macedo, D. F. (2021) · International Journal of Network Management · doi:10.1002/nem.2155 · Cited by 1
A Pure Network-Based Approach to Achieve Always Best Quality Video Streaming
Nguyen-Duc, T., Doan-Thanh, X., & Do-Viet, H. (2021) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-77428-8_4 · Cited by 1
Improving User Experience when HTTP Adaptive Streaming Clients Compete for Bandwidth
Guguen, C. T., Houdaille, R., & Bolzer, F. L. (2015) · Broadcasting Convention, International (Conf. Publ. No. 428) · doi:10.1049/ibc.2015.0011 · Cited by 1
Smart Client-Server Protocol and Architecture for Adaptive Multimedia Streaming
ABDULLAH, M. T. (2018) · doi:10.4995/thesis/10251/103324 · Cited by 1
A User-experience Driven SSIM-Aware Adaptation Approach for DASH Video StreamingPreprint
Othman, M., Chen, K., & Mokraoui, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.05696 · Cited by 1
MONITORIZACIÓN DEL PLANO DE DATOS EN REDES DEFINIDAS POR SOFTWARE
García, I. R. (2015) · Docta Complutense (Universidad Complutense de Madrid)
Route optimization for DASH over Software Defined Networks
Çetinkaya, C., Ozveren, Y., & Sayit, M. F. (2015) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2015.7130380
Lin, K. H., & Chung, K.-H. (2015) · International Journal of Future Computer and Communication · doi:10.7763/ijfcc.2015.v4.372
On Crowdsourced Live Video Streaming with Heterogeneous Broadcasters and Viewers
He, Q. (2016) · Summit (Simon Fraser University)
Application-Aware Infrastructure Clustering for Cloud Service Placement to Enhance User QoE
Chemodanov, D., & Calyam, P. P. (2016) · International Teletraffic Congress · doi:10.1109/itc-28.2016.312
Virtualized cloud video services
Yılmaz, S., Sahin, K. E., Bagci, K. T., & Tekalp, A. M. (2017) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2017.7960240
Technologies and Architectures for Future IP Television Services
Sassatelli, L., & Montpetit, M. (2018) · doi:10.1002/9781119397939.ch12
Frehse, G., & Althoff, M. (2017) · Institute of Science and Technology Austria
Towards QoE-Aware Dynamic Adaptive Streaming Over HTTP
Sobhani, A. (2017) · uO Research (University of Ottawa) · doi:10.20381/ruor-20852
A Video-Quality Controller for QoE Enhancement in HTTP Adaptive Streaming
Kurosaka, T., Mori, S., & Bandai, M. (2017) · IEICE Transactions on Communications · doi:10.1587/transcom.2017ebp3099
Advanced Modelling of Adaptive Bitrate Selection
Sani, Y. (2017) · Lancaster EPrints (Lancaster University) · doi:10.17635/lancaster/thesis/139
Santos, C., Silva, F. S. D., & Neto, A. (2017) · ACM International Symposium on Mobility Management and Wireless Access (MobiWac) · doi:10.1145/3132062.3132072
A FRAMEWORK FOR ECONOMIC ANALYSIS OF NETWORK ARCHITECTURES
Karakuş, M. (2019) · Purdue e-Pubs (Purdue University System) · doi:10.25394/pgs.7396967.v1
Getting More Out of the Existing Internet Infrastructure To Improve User Experiences
Bozkurt, I. N. (2018)
Charting an Intent Driven NetworkPreprint
Elkhatib, Y., Gareth Tyson, A., & Coulson, G. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.05925
Self-citationPerformance Characterization of a Commercial Video Streaming ServicePreprint
Ghasemi, M., Kanuparthy, P., Mansy, A., Benson, T., & Rexford, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.04966
A QoE Fairness Approach for Adaptive Video Streaming
Fisher, D. G. (2019) · doi:10.11575/prism/37351
Systems and Methods for Measuring and Improving End-User Application Performance on Mobile Devices
Nikravesh, A. (2018) · Deep Blue (University of Michigan)
QoE-aware Bandwidth Allocation for Multiple Video Streaming Players over HTTP and SDN
Thinh, P. H., Huyền, T. T. T., Quang, N. N., Nam, P. N., Thang, T. C., Hùng, N. V., & Hương, T. T. (2020) · Advances in Science Technology and Engineering Systems Journal · doi:10.25046/aj060121
Wang, J., Xie, H., He, Y.-H., Zong, F., & Wang, Z. (2021) · IEEE Access · doi:10.1109/access.2021.3061762
Лемешко, О. В., Yevdokymenko, M., Yeremenko, O., Kunicina, N., & Žiravecka, A. (2021) · Latvian Journal of Physics and Technical Sciences · doi:10.2478/lpts-2021-0045
A bio-inspired managed video delivery service using HTTP-based adaptive streaming
Sani, Y., Quinlan, J. J., & Sreenan, C. J. (2022) · Multimedia Systems · doi:10.1007/s00530-022-00894-x
Mobile Oriented Future Internet (MOFI)
Koh, S. (2020) · MDPI eBooks · doi:10.3390/books978-3-03936-103-8
Analyzing Queueing Problems via Bandits With Linear Reward & Nonlinear Workload Fairness
Wang, X., Xie, H. L., & Lui, J. C. S. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3276158
Quality of experience recent trends and challenges
Al-Shammari, B. K. J., & Jaish, A. A. (2023) · AIP conference proceedings · doi:10.1063/5.0148077
Hooker, J. (2024) · Encyclopedia of Optimization · doi:10.1007/978-3-030-54621-2_904-1
Fuzzy-Based MEC-Assisted Video Adaptation Framework for HTTP Adaptive Streaming
Rahman, W. U. (2025) · Future Internet · doi:10.3390/fi17090410
Algoritmo de calidad de experiencia para transmisiones de vídeo en redes definidas por software
Fernández, J. A. P. (2014) · Docta Complutense (Universidad Complutense de Madrid)
Structure-Aided Reinforcement Learning for Media Streaming Over Wireless Edge Networks
Bura, A., Bobbili, S. C., Rameshkumar, S., Rengarajan, D., Kalathil, D. M., & Shakkottai, S. (2025) · IEEE Transactions on Networking · doi:10.1109/ton.2025.3648256
Multi-Criteria Optimization of Content Delivery within the Future Media Internet
Bruneau‐Queyreix, J. (2017) · theses.fr (ABES) · doi:10.70675/66faf773ze4dfz467czafcaz119590a03ccb
High-QoE Privacy-Preserving Video Streaming
Silva, S. D. (2020) · theses.fr (ABES) · doi:10.70675/5c4b3ac0zb491z4edfza6aaz822f23aaa3e9
Metadata
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
