The DOI matches its Crossref record; 143 of the 143 references it cites were found in a registry or an index.
A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP
Open accessAbstract
In this survey, we present state-of-the-art bitrate adaptation algorithms for HTTP adaptive streaming (HAS). As a key distinction from other streaming approaches, the bitrate adaptation algorithms in HAS are chiefly executed at each client, i.e., in a distributed manner. The objective of these algorithms is to ensure a high quality of experience (QoE) for viewers in the presence of bandwidth fluctuations due to factors like signal strength, network congestion, network reconvergence events, etc. While such fluctuations are common in public Internet, they can also occur in home networksor even managed networks where there is often admission control and QoS tools. Bitrate adaptation algorithms may take factors like bandwidth estimations, playback buffer fullness, device features, viewer preferences, and content features into account, albeit with different weights. Since the viewer's QoE needs to be determined in real-time during playback, objective metrics are generally used including number of buffer stalls, duration of startup delay, frequency and amount of quality oscillations, and video instability. By design, the standards for HAS do not mandate any particular adaptation algorithm, leaving it to system builders to innovate and implement their own method. This survey provides an overview of the different methods proposed over the last several years.
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.
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
A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,387
Overview of the High Efficiency Video Coding (HEVC) Standard
Sullivan, G. J., Ohm, J.-R., Han, W.-J., & Wiegand, T. (2012) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2012.2221191 · Cited by 8,263
Overview of the H.264/AVC video coding standard
Wiegand, T., Sullivan, G. J., Bjontegaard, G., & Luthra, A. (2003) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2003.815165 · Cited by 8,184
Software-Defined Networking: A Comprehensive Survey
Kreutz, D., Ramos, F. M. V., Esteves Verissimo, P., Esteve Rothenberg, C., Azodolmolky, S., & Uhlig, S. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2371999 · Cited by 5,050
Image Quality Metrics: PSNR vs. SSIM
Hore, A., & Ziou, D. (2010) · 2010 20th International Conference on Pattern Recognition · doi:10.1109/icpr.2010.579 · Cited by 4,943
Overview of the Scalable Video Coding Extension of the H.264/AVC Standard
Schwarz, H., Marpe, D., & Wiegand, T. (2007) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2007.905532 · Cited by 3,533
Doyle, J. C., Francis, B. A., & Tannenbaum, A. (1992) · Cited by 3,085
Scope of validity of PSNR in image/video quality assessment
Huynh-Thu, Q., & Ghanbari, M. (2008) · Electronics Letters · doi:10.1049/el:20080522 · Cited by 2,959
A survey of techniques for internet traffic classification using machine learning
Nguyen, T. T. T., & Armitage, G. (2008) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2008.080406 · Cited by 1,664
A tutorial on decomposition methods for network utility maximization
Palomar, D. P., & Chiang, M. (2006) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2006.879350 · Cited by 1,647
Cited by
Deep learning for edge computing applications: A State-of-the-Art survey
Wang, F., Zhang, M., Wang, X., Ma, X., & Liu, J. (2020) · IEEE Access · doi:10.1109/access.2020.2982411 · Cited by 259
Ahmad, S., Shakeel, I., Mehfuz, S., & Ahmad, J. (2023) · Computer Science Review · doi:10.1016/j.cosrev.2023.100568 · Cited by 197
Metadata
- Authors
- Bentaleb, A., Taani, B., Begen, A. C., Timmerer, C., & Zimmermann, R.
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2019
- Volume
- 21
- Issue
- 1
- Pages
- 562-585
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W2906364736
- Cited by
- 494
- References
- 143
- FWCI
- 24.18
- Type
- journal article
- Language
- en
- Open access
- bronze
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP. https://app.cochat.ai/papers/10.1109/comst.2018.2862938
- The paper
- Bentaleb, A., Taani, B., Begen, A. C., Timmerer, C., & Zimmermann, R. (2019). A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP. IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/comst.2018.2862938
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.