Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152984
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dc.contributor.authorGao, Guanyuen_US
dc.contributor.authorZhang, Huaizhengen_US
dc.contributor.authorHu, Hanen_US
dc.contributor.authorWen, Yonggangen_US
dc.contributor.authorCai, Jianfeien_US
dc.contributor.authorLuo, Chongen_US
dc.contributor.authorZeng, Wenjunen_US
dc.date.accessioned2021-10-27T05:42:20Z-
dc.date.available2021-10-27T05:42:20Z-
dc.date.issued2018-
dc.identifier.citationGao, G., Zhang, H., Hu, H., Wen, Y., Cai, J., Luo, C. & Zeng, W. (2018). Optimizing quality of experience for adaptive bitrate streaming via viewer interest inference. IEEE Transactions On Multimedia, 20(12), 3399-3413. https://dx.doi.org/10.1109/TMM.2018.2838330en_US
dc.identifier.issn1520-9210en_US
dc.identifier.urihttps://hdl.handle.net/10356/152984-
dc.description.abstractRate adaptation is widely adopted in video streaming to improve the quality of experience (QoE). However, most of the existing rate adaptation approaches neglect the underlying video semantic information. In fact, influenced by video semantics and viewer preferences, the viewer may have different degrees of interest on different parts of a video. The interesting parts of a video can draw more visual attention from the viewer and have higher visual importance. As such, delivering the parts of a video that are interesting to the viewer in a higher quality can improve the perceptual video quality, compared with the semantics-agnostic approaches that treat each part of a video equally. Thus, it is natural to wonder: how to allocate bitrate budgets temporally over a video session under time-varying bandwidth while considering viewer interest? As an exploratory study, we propose an interest-aware rate adaptation approach for improving QoE by inferring viewer interest based on video semantics. We adopt the deep learning method to recognize the scenes of video frames and leverage the term frequency-inverse document frequency method to analyze the degrees of an individual viewer's interest on different types of scenes. The bandwidth, buffer occupancy, and viewer interest are jointly considered under the model predictive control framework for selecting appropriate bitrates for maximizing QoE. The objective and subjective evaluations measured in a real environment show that our method can achieve a higher QoE compared with the semantics-agnostic approaches.en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Multimediaen_US
dc.rights© 2018 IEEE. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleOptimizing quality of experience for adaptive bitrate streaming via viewer interest inferenceen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1109/TMM.2018.2838330-
dc.identifier.scopus2-s2.0-85047211386-
dc.identifier.issue12en_US
dc.identifier.volume20en_US
dc.identifier.spage3399en_US
dc.identifier.epage3413en_US
dc.subject.keywordsAdaptive Video Streamingen_US
dc.subject.keywordsRate Adaptationen_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
Appears in Collections:SCSE Journal Articles

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