Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152992
Title: Content-aware personalised rate adaptation for adaptive streaming via deep video analysis
Authors: Gao, Guanyu
Dong, Linsen
Zhang, Huaizheng
Wen, Yonggang
Zeng, Wenjun
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Gao, 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.2019.8761156
Abstract: Adaptive 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.
URI: https://hdl.handle.net/10356/152992
ISBN: 9781538680889
DOI: 10.1109/ICC.2019.8761156
Rights: © 2019 IEEE. All righs reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

Page view(s)

102
Updated on Jun 26, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.