Adaptive Bitrate Streaming for Energy-Efficiency Mobile Augmented Reality
With the development of the mobile augmented reality (MAR),users have higher requirements on video quality and response time on it.MAR applications offload computation-intensive tasks to the cloud or edge servers for processing.In order to provide users with high-quality rendering services,MAR needs...
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| Format: | Article |
| Language: | zho |
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Editorial office of Computer Science
2022-01-01
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| Series: | Jisuanji kexue |
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| Online Access: | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-1-194.pdf |
| _version_ | 1818284629241626624 |
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| author | CHEN Le, GAO Ling, REN Jie, DANG Xin, WANG Yi-hao, CAO Rui, ZHENG Jie, WANG Hai |
| author_facet | CHEN Le, GAO Ling, REN Jie, DANG Xin, WANG Yi-hao, CAO Rui, ZHENG Jie, WANG Hai |
| author_sort | CHEN Le, GAO Ling, REN Jie, DANG Xin, WANG Yi-hao, CAO Rui, ZHENG Jie, WANG Hai |
| collection | DOAJ |
| description | With the development of the mobile augmented reality (MAR),users have higher requirements on video quality and response time on it.MAR applications offload computation-intensive tasks to the cloud or edge servers for processing.In order to provide users with high-quality rendering services,MAR needs to download massive amounts of data from cloud or edge servers.Due to the instability of network condition and the limitation of network bandwidth,data transmission will extend MAR application response time,which increases the energy consumption,and seriously affects the user experience.This paper proposes a bit-rate adaptive model based on gradient boosting regression (GBR).The model considers the different needs of users in different network conditions,analyzes the features of the 200 popular videos,finds the connection between the video features and the user requirements,and provides appropriate video bitrate configuration according to different needs,thus to achieve the goal of maintaining experience,reducing latency and saving energy.The results show that compared with the original rendered 1080p video,the proposed bitrate adaptive model can save 58% downloading time latency(19.13 ms) per frame while maintaining the user's viewing experience as much as possible. |
| first_indexed | 2024-12-13T00:55:50Z |
| format | Article |
| id | doaj.art-6bba06fa324049cd8ea1d706c65d3fc7 |
| institution | Directory Open Access Journal |
| issn | 1002-137X |
| language | zho |
| last_indexed | 2024-12-13T00:55:50Z |
| publishDate | 2022-01-01 |
| publisher | Editorial office of Computer Science |
| record_format | Article |
| series | Jisuanji kexue |
| spelling | doaj.art-6bba06fa324049cd8ea1d706c65d3fc72022-12-22T00:04:48ZzhoEditorial office of Computer ScienceJisuanji kexue1002-137X2022-01-0149119420310.11896/jsjkx.201100107Adaptive Bitrate Streaming for Energy-Efficiency Mobile Augmented RealityCHEN Le, GAO Ling, REN Jie, DANG Xin, WANG Yi-hao, CAO Rui, ZHENG Jie, WANG Hai01 School of Information Science and Technology,Northwest University,State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services,Xi'an 710127,China<br/>2 College of Computer Science State-Province,Xi'an Polytechnic University,Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services,Xi'an 710600,China<br/>3 School of Computer Science,Shaanxi Normal University,Xi'an 710119,ChinaWith the development of the mobile augmented reality (MAR),users have higher requirements on video quality and response time on it.MAR applications offload computation-intensive tasks to the cloud or edge servers for processing.In order to provide users with high-quality rendering services,MAR needs to download massive amounts of data from cloud or edge servers.Due to the instability of network condition and the limitation of network bandwidth,data transmission will extend MAR application response time,which increases the energy consumption,and seriously affects the user experience.This paper proposes a bit-rate adaptive model based on gradient boosting regression (GBR).The model considers the different needs of users in different network conditions,analyzes the features of the 200 popular videos,finds the connection between the video features and the user requirements,and provides appropriate video bitrate configuration according to different needs,thus to achieve the goal of maintaining experience,reducing latency and saving energy.The results show that compared with the original rendered 1080p video,the proposed bitrate adaptive model can save 58% downloading time latency(19.13 ms) per frame while maintaining the user's viewing experience as much as possible.https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-1-194.pdfmobile augmented reality|performance optimization|gradient boosting regression|bitrate adaptive control |
| spellingShingle | CHEN Le, GAO Ling, REN Jie, DANG Xin, WANG Yi-hao, CAO Rui, ZHENG Jie, WANG Hai Adaptive Bitrate Streaming for Energy-Efficiency Mobile Augmented Reality Jisuanji kexue mobile augmented reality|performance optimization|gradient boosting regression|bitrate adaptive control |
| title | Adaptive Bitrate Streaming for Energy-Efficiency Mobile Augmented Reality |
| title_full | Adaptive Bitrate Streaming for Energy-Efficiency Mobile Augmented Reality |
| title_fullStr | Adaptive Bitrate Streaming for Energy-Efficiency Mobile Augmented Reality |
| title_full_unstemmed | Adaptive Bitrate Streaming for Energy-Efficiency Mobile Augmented Reality |
| title_short | Adaptive Bitrate Streaming for Energy-Efficiency Mobile Augmented Reality |
| title_sort | adaptive bitrate streaming for energy efficiency mobile augmented reality |
| topic | mobile augmented reality|performance optimization|gradient boosting regression|bitrate adaptive control |
| url | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-1-194.pdf |
| work_keys_str_mv | AT chenlegaolingrenjiedangxinwangyihaocaoruizhengjiewanghai adaptivebitratestreamingforenergyefficiencymobileaugmentedreality |