A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming
Video streaming has become a kind of main information carried by Unmanned Aerial Vehicles (UAVs). Unlike single transmission, when a cluster of UAVs execute the real-time video shooting and uploading mission, the insufficiency of wireless channel resources will lead to bandwidth competition among th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/15/3394 |
| _version_ | 1818014636915556352 |
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| author | Chao He Zhidong Xie Chang Tian |
| author_facet | Chao He Zhidong Xie Chang Tian |
| author_sort | Chao He |
| collection | DOAJ |
| description | Video streaming has become a kind of main information carried by Unmanned Aerial Vehicles (UAVs). Unlike single transmission, when a cluster of UAVs execute the real-time video shooting and uploading mission, the insufficiency of wireless channel resources will lead to bandwidth competition among them and the competition will bring bad watching experience to the audience. Therefore, how to allocate uplink bandwidth reasonably in the cluster has become a crucial problem. In this paper, an intelligent and distributed allocation mechanism is designed for improving users’ video viewing satisfication. Each UAV in a cluster can independently adjust and select its video encoding rate so as to achieve flexible uplink allocation. This choice relies neither on the existence of the central node, nor on the large amount of information interaction between UAVs. Firstly, in order to distinguish video service from ordinary data, a utility function for the overall Quality of Experience (QoE) is proposed. Then, a potential game model is built around the problem. By a distributed self-learning algorithm with low complexity, all UAVs can iteratively update their own bandwidth strategy in a short time until equilibria, thus achieving the total quality optimization of all videos. Numeric simulation results indicate, after a few iterations, that the algorithm converges to a set of correlation equilibria. This mechanism not only solves the uplink allocation problem of video streaming in UAV cluster, but also guarantees the wireless resource providers in distinguishing and ensuring network service quality. |
| first_indexed | 2024-04-14T06:46:43Z |
| format | Article |
| id | doaj.art-8ef16c185e75446eaf97fc8651ae8c54 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T06:46:43Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8ef16c185e75446eaf97fc8651ae8c542022-12-22T02:07:08ZengMDPI AGSensors1424-82202019-08-011915339410.3390/s19153394s19153394A QoE-Oriented Uplink Allocation for Multi-UAV Video StreamingChao He0Zhidong Xie1Chang Tian2College of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, ChinaCollege of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, ChinaCollege of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, ChinaVideo streaming has become a kind of main information carried by Unmanned Aerial Vehicles (UAVs). Unlike single transmission, when a cluster of UAVs execute the real-time video shooting and uploading mission, the insufficiency of wireless channel resources will lead to bandwidth competition among them and the competition will bring bad watching experience to the audience. Therefore, how to allocate uplink bandwidth reasonably in the cluster has become a crucial problem. In this paper, an intelligent and distributed allocation mechanism is designed for improving users’ video viewing satisfication. Each UAV in a cluster can independently adjust and select its video encoding rate so as to achieve flexible uplink allocation. This choice relies neither on the existence of the central node, nor on the large amount of information interaction between UAVs. Firstly, in order to distinguish video service from ordinary data, a utility function for the overall Quality of Experience (QoE) is proposed. Then, a potential game model is built around the problem. By a distributed self-learning algorithm with low complexity, all UAVs can iteratively update their own bandwidth strategy in a short time until equilibria, thus achieving the total quality optimization of all videos. Numeric simulation results indicate, after a few iterations, that the algorithm converges to a set of correlation equilibria. This mechanism not only solves the uplink allocation problem of video streaming in UAV cluster, but also guarantees the wireless resource providers in distinguishing and ensuring network service quality.https://www.mdpi.com/1424-8220/19/15/3394UAV clusteruplink allocationQoEpotential gamedistributed self-learning algorithmH.265 |
| spellingShingle | Chao He Zhidong Xie Chang Tian A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming Sensors UAV cluster uplink allocation QoE potential game distributed self-learning algorithm H.265 |
| title | A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming |
| title_full | A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming |
| title_fullStr | A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming |
| title_full_unstemmed | A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming |
| title_short | A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming |
| title_sort | qoe oriented uplink allocation for multi uav video streaming |
| topic | UAV cluster uplink allocation QoE potential game distributed self-learning algorithm H.265 |
| url | https://www.mdpi.com/1424-8220/19/15/3394 |
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