The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
SDNDASH: Improving QoE of HTTP Adaptive Streaming Using Software Defined Networking
Abstract
HTTP adaptive streaming (HAS) is being adopted with increasing frequency and becoming the de-facto standard for video streaming. However, the client-driven, on-off adaptation behavior of HAS results in uneven bandwidth competition and this is exacerbated when a large number of clients share the same bottleneck network link and compete for the available bandwidth. With HAS each client independently strives to maximize its individual share of the available bandwidth, which leads to bandwidth competition and a decrease in end-user quality of experience (QoE). The competition causes scalability issues, which are quality instability, unfair bandwidth sharing and network resource underutilization. We propose a new software defined networking (SDN) based dynamic resource allocation and management architecture for HAS systems, which aims to alleviate these scalability issues and improve the per-client QoE. Our architecture manages and allocates the network resources dynamically for each client based on its expected QoE. Experimental results show that the proposed architecture significantly enhances scalability by improving per-client QoE by at least 30% and supporting up to 80% more clients with the same QoE compared to the conventional schemes.
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“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 [198]
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“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 [198]
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Cites
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A Survey on Quality of Experience of HTTP Adaptive Streaming
Seufert, M., Egger, S., Slanina, M., Zinner, T., Hoßfeld, T., & Tran-Gia, P. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2360940 · Cited by 844
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Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE
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Self-citationMok, R. K. P., Luo, X., Chan, E. W. W., & Chang, R. K. C. (2012) · ACM Multimedia Systems Conference (MMSys) · doi:10.1145/2155555.2155558 · Cited by 331
Towards network-wide QoE fairness using openflow-assisted adaptive video streaming
Georgopoulos, P., Elkhatib, Y., Broadbent, M., Mu, M., & Race, N. (2013) · Proceedings of the 2013 ACM SIGCOMM workshop on Future human-centric multimedia networking · doi:10.1145/2491172.2491181 · Cited by 242
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Cited by
Sorted by citations
A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP
Bentaleb, A., Taani, B., Begen, A. C., Timmerer, C., & Zimmermann, R. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2862938 · Cited by 498
Self-citationQoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey
Barakabitze, A. A., Barman, N., Ahmad, A., Zadtootaghaj, S., Sun, L., Martini, M. G., & Atzori, L. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2958784 · Cited by 239
A Quality-of-Experience Database for Adaptive Video Streaming
Duanmu, Z., Rehman, A., & Wang, Z. (2018) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2018.2822870 · Cited by 146
Comyco: Quality-Aware Adaptive Video Streaming via Imitation Learning
Huang, T., Zhou, C., Zhang, R.-X., Wu, C., Yao, X., & Sun, L. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3351014 · Cited by 124
A Survey of Emerging Concepts and Challenges for QoE Management of Multimedia Services
Skorin-Kapov, L., Varela, M., Hoßfeld, T., & Chen, K.-T. (2018) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3176648 · Cited by 116
Metadata
- Authors
- Bentaleb, A., Begen, A. C., & Zimmermann, R.
- Affiliations
- National University of Singapore (SG)
- Özyeğin University (TR)
- Venue
- Proceedings of the 24th ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2016-10
- Pages
- 1296-1305
- OpenAlex
- W2526384825
- Cited by
- 160
- References
- 18
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 11 · 2023: 10 · 2022: 15 · 2021: 16 · 2020: 28 · 2019: 25 · 2018: 34 · 2017: 12
- FWCI
- 20.22
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Multimedia (ACM MM) (publishedVersion)
- eResearch@Ozyegin (submittedVersion)
- Funders
- National University of Singapore (T1251RES/4/5)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- 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
- Keywords
- HTTP adaptive streaming, software-defined networking, bandwidth competition, quality of experience, dynamic resource allocation, scalability, video streaming, network resource management
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