Affective Content-aware Adaptation Scheme on QoE Optimization of Adaptive Streaming over HTTP

Hu, S; Xu, M; Zhang, H; Xiao, C; Gui, C

Affective Content-aware Adaptation Scheme on QoE Optimization of Adaptive Streaming over HTTP

Hu, S Xu, M

Zhang, H Xiao, C Gui, C

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Publisher:: Association for Computing Machinery
Publication Type:: Journal Article
Citation:: ACM Transactions on Multimedia Computing Communications and Applications, 2019, 15 (3s), pp. 100 - 118
Issue Date:: 2019-12-07

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Field	Value	Language
dc.contributor.author	Hu, S	en_US
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.contributor.author	Zhang, H	en_US
dc.contributor.author	Xiao, C	en_US
dc.contributor.author	Gui, C	en_US
dc.date.issued	2019-12-07	en_US
dc.identifier.citation	ACM Transactions on Multimedia Computing Communications and Applications, 2019, 15 (3s), pp. 100 - 118	en_US
dc.identifier.issn	1551-6857	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138346
dc.description.abstract	The article presents a novel affective content-aware adaptation scheme (ACAA) to optimize Quality of Experience (QoE) for dynamic adaptive video streaming over HTTP (DASH). Most of the existing DASH adaptation schemes conduct video bit-rate adaptation based on an estimation of available network resources, which ignore user preference on affective content (AC) embedded in video data streaming over the network. Since the personal demands to AC is very different among all viewers, to satisfy individual affective demand is critical to improve the QoE in commercial video services. However, the results of video affective analysis cannot be applied into a current adaptive streaming scheme directly. Correlating the AC distributions in user's viewing history to each being streamed segment, the affective relevancy can be inferred as an affective metric for the AC related segment. Further, we have proposed an ACAA scheme to optimize QoE for user desired affective content while taking into account both network status and affective relevancy. We have implemented the ACAA scheme over a realistic trace-based evaluation and compared its performance in terms of network performance, QoE with that of Probe and Adaptation (PANDA), buffer-based adaptation (BBA), and Model Predictive Control (MPC). Experimental results show that ACAA can preserve available buffer time for future being delivered affective content preferred by viewer's individual preference to achieve better QoE in affective contents than those normal contents while remain the overall QoE to be satisfactory.	en_US
dc.language	en	en_US
dc.publisher	Association for Computing Machinery	en_US
dc.relation.ispartof	ACM Transactions on Multimedia Computing Communications and Applications	en_US
dc.relation.isbasedon	10.1145/3328997	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Affective Content-aware Adaptation Scheme on QoE Optimization of Adaptive Streaming over HTTP	en_US
dc.type	Journal Article
utslib.citation.volume	3s	en_US
utslib.citation.volume	15	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true	en_US
pubs.issue	3s	en_US
pubs.publication-status	Published	en_US
pubs.volume	15	en_US

Abstract:

The article presents a novel affective content-aware adaptation scheme (ACAA) to optimize Quality of Experience (QoE) for dynamic adaptive video streaming over HTTP (DASH). Most of the existing DASH adaptation schemes conduct video bit-rate adaptation based on an estimation of available network resources, which ignore user preference on affective content (AC) embedded in video data streaming over the network. Since the personal demands to AC is very different among all viewers, to satisfy individual affective demand is critical to improve the QoE in commercial video services. However, the results of video affective analysis cannot be applied into a current adaptive streaming scheme directly. Correlating the AC distributions in user's viewing history to each being streamed segment, the affective relevancy can be inferred as an affective metric for the AC related segment. Further, we have proposed an ACAA scheme to optimize QoE for user desired affective content while taking into account both network status and affective relevancy. We have implemented the ACAA scheme over a realistic trace-based evaluation and compared its performance in terms of network performance, QoE with that of Probe and Adaptation (PANDA), buffer-based adaptation (BBA), and Model Predictive Control (MPC). Experimental results show that ACAA can preserve available buffer time for future being delivered affective content preferred by viewer's individual preference to achieve better QoE in affective contents than those normal contents while remain the overall QoE to be satisfactory.

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http://hdl.handle.net/10453/138346