Publication: Understanding quality of experience of ...
Master data
Title: | Understanding quality of experience of heuristic-based HTTP adaptive bitrate algorithms |
Subtitle: | |
Abstract: | Adaptive bitrate (ABR) algorithms play a crucial role in delivering the highest possible viewer's Quality of Experience (QoE) in HTTP Adaptive Streaming (HAS). Online video streaming service providers use HAS - the dominant video streaming technique on the Internet - to deliver the best QoE for their users. A viewer's delight relies heavily on how the ABR of a media player can adapt the stream's quality to the current network conditions. QoE for video streaming sessions has been assessed in many research projects to give better insight into the significant quality metrics such as startup delay and stall events. The ITU Telecommunication Standardization Sector (ITU-T) P.1203 quality evaluation model allows to algorithmically predict a subjective Mean Opinion Score (MOS) by considering various quality metrics. Subjective evaluation is the best assessment method for examining the end-user opinion over a video streaming session's experienced quality. We have conducted subjective evaluations with crowdsourced participants and evaluated the MOS of the sessions using the ITU-T P.1203 quality model. This paper's main contribution is to investigate the correspondence of subjective and objective evaluations for well-known heuristic-based ABRs. |
Keywords: | HTTP Adaptive Streaming, ABR Algorithms, Quality of Experience, Crowdsourcing, Subjective Evaluation, Objective Evaluation, MOS |
Publication type: | Article in Proceedings (Authorship) |
Publication date: | 16.07.2021 (Online) |
Published by: |
Nossdav '21: Proceedings of the 31st ACM Workshop on Network and Operating Systems Support for Digital Audio and Video
Nossdav '21: Proceedings of the 31st ACM Workshop on Network and Operating Systems Support for Digital Audio and Video
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ACM Digital Library;
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Title of the series: | - |
Volume number: | - |
First publication: | Yes |
Version: | - |
Page: | pp. 82 - 89 |
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Keine Version vorhanden |
Publication date: | 16.07.2021 |
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eISSN: | - |
DOI: | http://dx.doi.org/10.1145/3458306.3458875 |
Homepage: | https://dl.acm.org/doi/10.1145/3458306.3458875 |
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Authors
Babak Taraghi (internal) | ||||
Abdelhak Bentaleb
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Christian Timmerer (internal) | ||||
Roger Zimmermann (external) | ||||
Hermann Hellwagner (internal) |
Assignment
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Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
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AT - 9020 Klagenfurt am Wörthersee |
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National University of Singapore
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SG - 119077 Singapur |
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