The DOI matches its Crossref record; 197 of the 199 references it cites were found in a registry or an index.
Adaptive Bitrate Selection: A Survey
Abstract
HTTP adaptive streaming (HAS) is the most recent attempt regarding video quality adaptation. It enables cheap and easy to implement streaming technology without the need for a dedicated infrastructure. By using a combination of TCP and HTTP it has the advantage of reusing all the existing technologies designed for ordinary web. Equally important is that HAS traffic passes through firewalls and works well when NAT is deployed. The rate adaptation controller of HAS, commonly called adaptive bitrate selection (ABR), is currently receiving a lot of attention from both industry and academia. However, most of the research efforts concentrate on a specific aspect or a particular methodology without considering the overall context. This paper presents a comprehensive survey of the most significant research activities in the area of client-side HTTP-based adaptive video streaming. It starts by decomposing the ABR module into three subcomponents, namely: resource estimation function, chunk request scheduling, and adaptation module. Each subcomponent encapsulates a particular function that is vital to the operation of an ABR scheme. A review of each of the subcomponents and how they interact with each other is presented. Furthermore, those external factors that are known to have a direct impact on the performance of an ABR module, such as content nature, CDN, and context, are discussed. In conclusion, this paper provides an extensive reference for further research in the field.
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“Many studies have been focusing on enhancing the QoE by optimizing the video quality adaptation algorithms on the client and/or the server-side [6], [191].”
From QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey (2020) · cites this paper as [191]
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Cites
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Cited by
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QoE 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
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Jiang, X., Yu, F. R., Song, T., & Leung, V. C. M. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3065237 · Cited by 200
Wang, S., Bi, S., & Zhang, Y. A. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3126062 · Cited by 78
A Contemporary Survey on Live Video Streaming from a Computation-Driven Perspective
Dao, N., Tran, A.-T., Tu, N. H., Thanh, T. T., Bao, V. N. Q., & Cho, S. (2022) · ACM Computing Surveys · doi:10.1145/3519552 · Cited by 67
Metadata
- Authors
- Sani, Y., Mauthe, A., & Edwards, C.
- Affiliations
- Lancaster University (GB)
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2017
- Volume
- 19
- Issue
- 4
- Pages
- 2985-3014
- ISSN
- 1553-877X (ISSN-L)
- OpenAlex
- W2735173535
- Cited by
- 121
- References
- 199
- Citations by year
- 2026: 6 · 2025: 16 · 2024: 16 · 2023: 12 · 2022: 14 · 2021: 15 · 2020: 15 · 2019: 16 · 2018: 9 · 2017: 2
- FWCI
- 4.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- Funders
- Petroleum Technology Development Fund, Nigeria
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Computer Networks and Communications › Caching and Content Delivery
- Keywords
- HTTP adaptive streaming, video quality adaptation, resource estimation, content delivery networks, network context
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