Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152992
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dc.contributor.authorGao, Guanyuen_US
dc.contributor.authorDong, Linsenen_US
dc.contributor.authorZhang, Huaizhengen_US
dc.contributor.authorWen, Yonggangen_US
dc.contributor.authorZeng, Wenjunen_US
dc.date.accessioned2021-10-27T08:16:06Z-
dc.date.available2021-10-27T08:16:06Z-
dc.date.issued2019-
dc.identifier.citationGao, G., Dong, L., Zhang, H., Wen, Y. & Zeng, W. (2019). Content-aware personalised rate adaptation for adaptive streaming via deep video analysis. ICC 2019 - 2019 IEEE International Conference on Communications (ICC). https://dx.doi.org/10.1109/ICC.2019.8761156en_US
dc.identifier.isbn9781538680889-
dc.identifier.urihttps://hdl.handle.net/10356/152992-
dc.description.abstractAdaptive bitrate (ABR) streaming is the de facto solution for achieving smooth viewing experiences under unstable network conditions. However, most of the existing rate adaptation approaches for ABR are content-agnostic, without considering the semantic information of the video content. Nevertheless, semantic information largely determines the informativeness and interestingness of the video content, and consequently affects the QoE for video streaming. One common case is that the user may expect higher quality for the parts of video content that are more interesting or informative so as to reduce overall subjective quality loss. This creates two main challenges for such a problem: First, how to determine which parts of the video content are more interesting? Second, how to allocate bitrate budgets for different parts of the video content with different significances? To address these challenges, we propose a Content-of-Interest (CoI) based rate adaptation scheme for ABR. We first design a deep learning approach for recognizing the interestingness of the video content, and then design a Deep Q-Network (DQN) approach for rate adaptation by incorporating video interestingness information. The experimental results show that our method can recognize video interestingness precisely, and the bitrate allocation for ABR can be aligned with the interestingness of video content while not compromising the performances on objective QoE metrics.en_US
dc.language.isoenen_US
dc.rights© 2019 IEEE. All righs reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleContent-aware personalised rate adaptation for adaptive streaming via deep video analysisen_US
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.conferenceICC 2019 - 2019 IEEE International Conference on Communications (ICC)en_US
dc.identifier.doi10.1109/ICC.2019.8761156-
dc.identifier.scopus2-s2.0-85070191067-
dc.subject.keywordsStreaming Mediaen_US
dc.subject.keywordsBit Rateen_US
dc.citation.conferencelocationShanghai, Chinaen_US
dc.description.acknowledgementThis project is partially funded by Microsoft Research Asia.en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
Appears in Collections:SCSE Conference Papers

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