Reinforcement learning-based real-time video streaming control and on-device training research
Service platforms centered on the Internet of things and mobile Internet are in accelerating process.Hundreds of millions of end-users communicate through network real-time video services, which have become an irreplaceable core tool in human’s digital life.However, the Internet is becoming dynamic,...
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2022-12-01
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| Series: | 物联网学报 |
| Subjects: | |
| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2022.00306/ |
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| author | Huanhuan ZHANG Anfu ZHOU Huadong MA |
| author_facet | Huanhuan ZHANG Anfu ZHOU Huadong MA |
| author_sort | Huanhuan ZHANG |
| collection | DOAJ |
| description | Service platforms centered on the Internet of things and mobile Internet are in accelerating process.Hundreds of millions of end-users communicate through network real-time video services, which have become an irreplaceable core tool in human’s digital life.However, the Internet is becoming dynamic, and heterogeneous, which imposes stringent requirements on real-time video streaming control technology.Moreover, the QoE of real-time video is not satisfactory.An adaptive reinforcement learning-based video intelligent transmission algorithm was designed, which can deal with heterogeneous network environment.And then, an effective end-to-end on-device training framework was designed to decrease server overhead, and a detailed evaluation and analysis on the neural network design and structure was provided.Experimental results show that the proposed algorithm can effectively predict heterogeneous network bandwidth, and reduces the bandwidth prediction error by 48.48%, comparing with the representative streaming control algorithm.The effective bandwidth prediction can further improve the user QoE, such as improving the video fluency by 60.65%, and improving the video quality by 16.52%.Besides, the analysis can provide empirical insights for further study, and holds potential to push the development of intelligent video applications. |
| format | Article |
| id | doaj-art-f1204ade5c5748cbb833a15f536a0ec9 |
| institution | Kabale University |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2022-12-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-f1204ade5c5748cbb833a15f536a0ec92025-01-15T02:54:43ZzhoChina InfoCom Media Group物联网学报2096-37502022-12-01611359580186Reinforcement learning-based real-time video streaming control and on-device training researchHuanhuan ZHANGAnfu ZHOUHuadong MAService platforms centered on the Internet of things and mobile Internet are in accelerating process.Hundreds of millions of end-users communicate through network real-time video services, which have become an irreplaceable core tool in human’s digital life.However, the Internet is becoming dynamic, and heterogeneous, which imposes stringent requirements on real-time video streaming control technology.Moreover, the QoE of real-time video is not satisfactory.An adaptive reinforcement learning-based video intelligent transmission algorithm was designed, which can deal with heterogeneous network environment.And then, an effective end-to-end on-device training framework was designed to decrease server overhead, and a detailed evaluation and analysis on the neural network design and structure was provided.Experimental results show that the proposed algorithm can effectively predict heterogeneous network bandwidth, and reduces the bandwidth prediction error by 48.48%, comparing with the representative streaming control algorithm.The effective bandwidth prediction can further improve the user QoE, such as improving the video fluency by 60.65%, and improving the video quality by 16.52%.Besides, the analysis can provide empirical insights for further study, and holds potential to push the development of intelligent video applications.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2022.00306/real-time videoadaptive streaming controlquality-of-experiencereinforcement learningon-device training |
| spellingShingle | Huanhuan ZHANG Anfu ZHOU Huadong MA Reinforcement learning-based real-time video streaming control and on-device training research 物联网学报 real-time video adaptive streaming control quality-of-experience reinforcement learning on-device training |
| title | Reinforcement learning-based real-time video streaming control and on-device training research |
| title_full | Reinforcement learning-based real-time video streaming control and on-device training research |
| title_fullStr | Reinforcement learning-based real-time video streaming control and on-device training research |
| title_full_unstemmed | Reinforcement learning-based real-time video streaming control and on-device training research |
| title_short | Reinforcement learning-based real-time video streaming control and on-device training research |
| title_sort | reinforcement learning based real time video streaming control and on device training research |
| topic | real-time video adaptive streaming control quality-of-experience reinforcement learning on-device training |
| url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2022.00306/ |
| work_keys_str_mv | AT huanhuanzhang reinforcementlearningbasedrealtimevideostreamingcontrolandondevicetrainingresearch AT anfuzhou reinforcementlearningbasedrealtimevideostreamingcontrolandondevicetrainingresearch AT huadongma reinforcementlearningbasedrealtimevideostreamingcontrolandondevicetrainingresearch |