Mobile live video streaming optimization via crowdsourcing brokerage

Wu, T; Dou, W; Ni, Q; Yu, S; Chen, G

Mobile live video streaming optimization via crowdsourcing brokerage

Wu, T Dou, W Ni, Q Yu, S

Chen, G

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Multimedia, 2017, 19, (10), pp. 2267-2281
Issue Date:: 2017-10-01

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Field	Value	Language
dc.contributor.author	Wu, T
dc.contributor.author	Dou, W
dc.contributor.author	Ni, Q
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.contributor.author	Chen, G
dc.date.accessioned	2021-04-29T00:23:28Z
dc.date.available	2021-04-29T00:23:28Z
dc.date.issued	2017-10-01
dc.identifier.citation	IEEE Transactions on Multimedia, 2017, 19, (10), pp. 2267-2281
dc.identifier.issn	1520-9210
dc.identifier.issn	1941-0077
dc.identifier.uri	http://hdl.handle.net/10453/148528
dc.description.abstract	Nowadays, people can enjoy a rich real-time sensing cognition of what they are interested in anytime and anywhere by leveraging powerful mobile devices such as smartphones. As a key support for the propagation of these richer live media contents, cellular-based access technologies play a vital role to provide reliable and ubiquitous Internet access to mobile devices. However, these limited wireless network channel conditions vary and fluctuate depending on weather, building shields, congestion, etc., which degrade the quality of live video streaming dramatically. To address this challenge, we propose to use crowdsourcing brokerage in future networks which can improve each mobile user's bandwidth condition and reduce the fluctuation of network condition. Further, to serve mobile users better in this crowdsourcing style, we study the brokerage scheduling problem which aims at maximizing the user's quality of experience satisfaction degree cost effectively. Both offline and online algorithms are proposed to solve this problem. The results of extensive evaluations demonstrate that by leveraging crowdsourcing technique, our solution can cost-effectively guarantee a higher quality view experience.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Multimedia
dc.relation.isbasedon	10.1109/TMM.2017.2736963
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Mobile live video streaming optimization via crowdsourcing brokerage
dc.type	Journal Article
utslib.citation.volume	19
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
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 Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2021-04-29T00:23:27Z
pubs.issue	10
pubs.publication-status	Published
pubs.volume	19
utslib.citation.issue	10

Abstract:

Nowadays, people can enjoy a rich real-time sensing cognition of what they are interested in anytime and anywhere by leveraging powerful mobile devices such as smartphones. As a key support for the propagation of these richer live media contents, cellular-based access technologies play a vital role to provide reliable and ubiquitous Internet access to mobile devices. However, these limited wireless network channel conditions vary and fluctuate depending on weather, building shields, congestion, etc., which degrade the quality of live video streaming dramatically. To address this challenge, we propose to use crowdsourcing brokerage in future networks which can improve each mobile user's bandwidth condition and reduce the fluctuation of network condition. Further, to serve mobile users better in this crowdsourcing style, we study the brokerage scheduling problem which aims at maximizing the user's quality of experience satisfaction degree cost effectively. Both offline and online algorithms are proposed to solve this problem. The results of extensive evaluations demonstrate that by leveraging crowdsourcing technique, our solution can cost-effectively guarantee a higher quality view experience.

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http://hdl.handle.net/10453/148528