Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/655797
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dc.contributor.authorJoohyun Leeen_US
dc.contributor.authorKyunghan Leeen_US
dc.contributor.authorChoongwoo Hanen_US
dc.contributor.authorTaehoon Kimen_US
dc.contributor.authorSong Chongen_US
dc.date.accessioned2020-05-20T10:26:36Z-
dc.date.available2020-05-20T10:26:36Z-
dc.date.issued2016en_US
dc.identifier.issn1520-9210en_US
dc.identifier.issn1941-0077en_US
dc.identifier.other10.1109/TMM.2016.2604565en_US
dc.identifier.urihttp://localhost/handle/Hannan/160266en_US
dc.identifier.urihttp://localhost/handle/Hannan/655797-
dc.description.abstractFrom the advancements of mobile display and network infrastructure, mobile users can enjoy high quality mobile video streaming anywhere, anytime. However, most mobile users are still reluctant to use high quality video streaming when they are mobile due to costly cellular data and high energy consumption. In this work, we develop scheduling algorithms for resource-efficient mobile video streaming, which minimize the weighted sum objective of cellular cost and energy consumption. We first model the scheduling problem as a Markov decision process and propose an optimal scheduling algorithm based on dynamic programming. Then, we derive a heuristic algorithm that approximates the optimal algorithm. To evaluate the performance of proposed algorithms, we run simulation over YouTube video traces with audience retention graphs and mobility/connectivity traces in public transportation (e.g., commuting). Through extensive simulations, we show that our proposed scheduling algorithm has negligible performance loss compared to the optimal scheduling algorithm, where it saves 59% of cellular cost and 41% of energy compared to the YouTube default scheduler. We also implement our scheduling algorithm on an Android platform, and experimentally evaluate the performance compared to existing streaming policies.en_US
dc.publisherIEEEen_US
dc.relation.haspart7556972.pdfen_US
dc.subjectresource efficiency|Markov decision process|mobile video streaming|Communication energy savingen_US
dc.titleResource-Efficient Mobile Multimedia Streaming With Adaptive Network Selectionen_US
dc.typeArticleen_US
dc.journal.volume18en_US
dc.journal.issue12en_US
dc.journal.titleIEEE Transactions on Multimediaen_US
Appears in Collections:2016

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorJoohyun Leeen_US
dc.contributor.authorKyunghan Leeen_US
dc.contributor.authorChoongwoo Hanen_US
dc.contributor.authorTaehoon Kimen_US
dc.contributor.authorSong Chongen_US
dc.date.accessioned2020-05-20T10:26:36Z-
dc.date.available2020-05-20T10:26:36Z-
dc.date.issued2016en_US
dc.identifier.issn1520-9210en_US
dc.identifier.issn1941-0077en_US
dc.identifier.other10.1109/TMM.2016.2604565en_US
dc.identifier.urihttp://localhost/handle/Hannan/160266en_US
dc.identifier.urihttp://localhost/handle/Hannan/655797-
dc.description.abstractFrom the advancements of mobile display and network infrastructure, mobile users can enjoy high quality mobile video streaming anywhere, anytime. However, most mobile users are still reluctant to use high quality video streaming when they are mobile due to costly cellular data and high energy consumption. In this work, we develop scheduling algorithms for resource-efficient mobile video streaming, which minimize the weighted sum objective of cellular cost and energy consumption. We first model the scheduling problem as a Markov decision process and propose an optimal scheduling algorithm based on dynamic programming. Then, we derive a heuristic algorithm that approximates the optimal algorithm. To evaluate the performance of proposed algorithms, we run simulation over YouTube video traces with audience retention graphs and mobility/connectivity traces in public transportation (e.g., commuting). Through extensive simulations, we show that our proposed scheduling algorithm has negligible performance loss compared to the optimal scheduling algorithm, where it saves 59% of cellular cost and 41% of energy compared to the YouTube default scheduler. We also implement our scheduling algorithm on an Android platform, and experimentally evaluate the performance compared to existing streaming policies.en_US
dc.publisherIEEEen_US
dc.relation.haspart7556972.pdfen_US
dc.subjectresource efficiency|Markov decision process|mobile video streaming|Communication energy savingen_US
dc.titleResource-Efficient Mobile Multimedia Streaming With Adaptive Network Selectionen_US
dc.typeArticleen_US
dc.journal.volume18en_US
dc.journal.issue12en_US
dc.journal.titleIEEE Transactions on Multimediaen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7556972.pdf1.45 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorJoohyun Leeen_US
dc.contributor.authorKyunghan Leeen_US
dc.contributor.authorChoongwoo Hanen_US
dc.contributor.authorTaehoon Kimen_US
dc.contributor.authorSong Chongen_US
dc.date.accessioned2020-05-20T10:26:36Z-
dc.date.available2020-05-20T10:26:36Z-
dc.date.issued2016en_US
dc.identifier.issn1520-9210en_US
dc.identifier.issn1941-0077en_US
dc.identifier.other10.1109/TMM.2016.2604565en_US
dc.identifier.urihttp://localhost/handle/Hannan/160266en_US
dc.identifier.urihttp://localhost/handle/Hannan/655797-
dc.description.abstractFrom the advancements of mobile display and network infrastructure, mobile users can enjoy high quality mobile video streaming anywhere, anytime. However, most mobile users are still reluctant to use high quality video streaming when they are mobile due to costly cellular data and high energy consumption. In this work, we develop scheduling algorithms for resource-efficient mobile video streaming, which minimize the weighted sum objective of cellular cost and energy consumption. We first model the scheduling problem as a Markov decision process and propose an optimal scheduling algorithm based on dynamic programming. Then, we derive a heuristic algorithm that approximates the optimal algorithm. To evaluate the performance of proposed algorithms, we run simulation over YouTube video traces with audience retention graphs and mobility/connectivity traces in public transportation (e.g., commuting). Through extensive simulations, we show that our proposed scheduling algorithm has negligible performance loss compared to the optimal scheduling algorithm, where it saves 59% of cellular cost and 41% of energy compared to the YouTube default scheduler. We also implement our scheduling algorithm on an Android platform, and experimentally evaluate the performance compared to existing streaming policies.en_US
dc.publisherIEEEen_US
dc.relation.haspart7556972.pdfen_US
dc.subjectresource efficiency|Markov decision process|mobile video streaming|Communication energy savingen_US
dc.titleResource-Efficient Mobile Multimedia Streaming With Adaptive Network Selectionen_US
dc.typeArticleen_US
dc.journal.volume18en_US
dc.journal.issue12en_US
dc.journal.titleIEEE Transactions on Multimediaen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7556972.pdf1.45 MBAdobe PDFThumbnail
Preview File