Computer Science ›› 2019, Vol. 46 ›› Issue (11): 88-93.doi: 10.11896/jsjkx.190500106

• Network & Communication • Previous Articles     Next Articles

Dynamic Resource Allocation for UAV Video Uploading

HE Chao1,2, XIE Zhi-dong1,2, TIAN Chang1   

  1. (College of Communications Engineering,Army Engineering University of PLA,Nanjing 210007,China)1
    (National Innovation Institute of Defense Technology,Academy of Military Sciences of PLA,Beijing 100071,China)2
  • Received:2019-05-20 Online:2019-11-15 Published:2019-11-14

Abstract: Unmanned Aerial Vehicles (UAV) can capture images and videos in real time by the sensors they carry.In particular,when a cluster of UAVs work together,they are able to efficiently complete reconnaissance,perception,mapping and other tasks,which make them widely used in both military and civil fields.However,all videos captured by UAVs need to be transmitted to the ground station or control center through wireless channels.The requirement of wireless channel transmission rate is higher and higher,along with video service definition unceasing enhancement and the cluster quantity continuous increase.Thus,under the constraint of limited wireless transmission resources,how to allocate them in the UAV cluster to maximize the uploading quality of videos has become an urgent problem to be solved.For this problem,a distributed algorithm was designed.In order to specify video from other data transmission,the QoE-oriented utility function is considered first.Then,around the problem,a potential game model is formulated and all the users can update their strategies with very little information exchange.The algorithm converges to a set of correlated equilibria and achieves the global optimal allocation of wireless resources in the cluster.This algorithm starts from the perspective of video application,and according to the properties of different video signals,each UAV can intelligently adjust the channel resource occupation.The highest total utility of the UAV cluster can be achieved under the limited wireless channel resources.Numeric simulation results indicate that it brings remarkable benefits to both resource providers and UAV video users.

Key words: Distributed algorithm, Potential game, Resource allocation, UAV, Video service

CLC Number: 

  • TN919
[1]美国国防部.陆军神目:美国陆军无人机系统2010-2035路线图[M].丁卫华,孟凡松,译.沈阳:辽宁大学出版社,2011:1-3.
[2]JIANG Q,LEUNG V CM,TANG H,et al.QoS-Guaranteed Adaptive Bandwidth Allocation for Mobile Multiuser Scalable Video Streaming [J].IEEE Wireless Communications Letters,2019,8(3):721-724.
[3]ZHU L,ZHAN C,HU H.Transmission Rate Allocation for Reliable Video Transmission in Aerial Vehicle Networks[C]∥2018 14th International Wireless Communications Mobile Computing Conference (IWCMC).Limassol,2018:30-35.
[4]BAI X,LI Q,TANG Y.A Low-Complexity Resource Allocation Algorithm for Indoor Visible Light Communication Ultra-Dense Networks [J].Applied Sciences,2019,9(7):1391-1408.
[5]ZHU K,NIYATO D,WANG P.Optimal Bandwidth Allocation with Dynamic Service Selection in Heterogeneous Wireless Networks[C]∥IEEE Global Telecommunications Conference (GLOBECOM 2010).Miami,2010:1-5.
[6]LI F W,HUANG X,ZHANG H B,et al.Cluster-based Radio Resource Allocation Mechanism in D2D Networks[J].Computer Science,2018,45(9):123-128,165.(in Chinese)
李方伟,黄旭,张海波,等.D2D网络中基于分簇的无线资源分配机制[J].计算机科学,2018,45(9):123-128,165.
[7]DONG C W,WEN W S.Joint optimization for task offloading in edge computing:An evolutionary game approach[J].Sensors,2019,19(3):740-763.
[8]SARMA A,CHAKRABORTY S,NANDI S.Deciding Handover Points Based on Context-Aware Load Balancing in a WiFi-WiMAX Heterogeneous Network Environment[J].IEEE Transactions on Vehicular Technology,2016,65(1):348-357.
[9]SENOUCI M A,SOUIHI S,HOCEINI S,et al.QoE-based network interface selection for heterogeneous wireless networks:A survey and e-Health case proposal[C]∥2016 IEEE Wireless Communications and Networking Conference.Doha,2016:1-6.
[10]DENG Z,LIU Y,LIU J,et al.QoE-Oriented Rate Allocation for Multipath High-Definition Video Streaming Over Heteroge-neous Wireless Access Networks[J].IEEE Systems Journal,2017,11(4):2524-2535..
[11]ELGABLIL A,ELGHARIANI A,AGGARWAL V,et al.QoE-Aware Resource Allocation for Small Cells[C]∥2018 IEEE Global Communications Conference (GLOBECOM).Abu Dhabi,2018:1-6.
[12]YUAN H,WEI X,YANG F,et al.Cooperative BargainingGame-Based Multiuser Bandwidth Allocation for Dynamic Adaptive Streaming Over HTTP[J].IEEE Transactions on Multimedia,2018,20(1):183-197.
[13]BIAN Y Y.5G Communication technology promotes the development of military UAVs[J].Military Abstract,2019(7):20-23.(in Chinese)
卞颖颖.5G通信技术促进军用无人机发展[J].军事文摘,2019(7):20-23.
[14]谢希仁.计算机网络(第7版)[M].北京:电子工业出版社,2017:375.
[15]XIONG L R,JIN X.QoE Evaluation Model of Mobile Streaming Media[J].Computer Science,2017,44(S2):110-114.(in Chinese)
熊丽荣,金鑫.移动流媒体用户QoE评估模型[J].计算机科学,2017,44(S2):110-114.
[16]SHOAIB K,SVETOSLAV D,ECKEHARD S,et al.MOS-Based Multiuser Multiapplication Cross-Layer Optimization for Mobile Multimedia Communication[J].Advances in Multimedia,2007,2007:1-11.
[17]THAKOLSRI S,KELLERER W,STEINBACH E.QoE-BasedCross-Layer Optimization of Wireless Video with Unperceivable Temporal Video Quality Fluctuation[C]∥2011 IEEE International Conference on Communications (ICC).Kyoto,2011:1-6.
[18]CHOI L U,IVRLAC M T,STEINBACH E,et al.Sequence-level models for distortion-rate behaviour of compressed video[C]∥IEEE International Conference on Image Processing.Genova,2005.
[19]VOORNEVELD M.Best-response potential games[J].Economics Letters,2000,66(3):289-295.
[20]SCUTARI G,BARBAROSSA S,PALOMAR D P.PotentialGames:A Framework for Vector Power Control Problems With Coupled Constraints[C]∥2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.Toulouse,2006.
[21]HART S,MAS-COLELL A.A Simple Adaptive ProcedureLeading to Correlated Equilibrium[J].Econometrica,2000,68(5):1127-1150.
[1] YU Bin, LI Xue-hua, PAN Chun-yu, LI Na. Edge-Cloud Collaborative Resource Allocation Algorithm Based on Deep Reinforcement Learning [J]. Computer Science, 2022, 49(7): 248-253.
[2] TANG Feng, FENG Xiang, YU Hui-qun. Multi-task Cooperative Optimization Algorithm Based on Adaptive Knowledge Transfer andResource Allocation [J]. Computer Science, 2022, 49(7): 254-262.
[3] LI Meng-fei, MAO Ying-chi, TU Zi-jian, WANG Xuan, XU Shu-fang. Server-reliability Task Offloading Strategy Based on Deep Deterministic Policy Gradient [J]. Computer Science, 2022, 49(7): 271-279.
[4] CHEN Bo-chen, TANG Wen-bing, HUANG Hong-yun, DING Zuo-hua. Pop-up Obstacles Avoidance for UAV Formation Based on Improved Artificial Potential Field [J]. Computer Science, 2022, 49(6A): 686-693.
[5] ZHOU Tian-qing, YUE Ya-li. Multi-Task and Multi-Step Computation Offloading in Ultra-dense IoT Networks [J]. Computer Science, 2022, 49(6): 12-18.
[6] QIU Xu, BIAN Hao-bu, WU Ming-xiao, ZHU Xiao-rong. Study on Task Offloading Algorithm for Internet of Vehicles on Highway Based on 5G MillimeterWave Communication [J]. Computer Science, 2022, 49(6): 25-31.
[7] XU Hao, CAO Gui-jun, YAN Lu, LI Ke, WANG Zhen-hong. Wireless Resource Allocation Algorithm with High Reliability and Low Delay for Railway Container [J]. Computer Science, 2022, 49(6): 39-43.
[8] SHEN Jia-fang, QIAN Li-ping, YANG Chao. Non-orthogonal Multiple Access and Multi-dimension Resource Optimization in EH Relay NB-IoT Networks [J]. Computer Science, 2022, 49(5): 279-286.
[9] ZHAO Geng, SONG Xin-yu, MA Ying-jie. Secure Data Link of Unmanned Aerial Vehicle Based on Chaotic Sub-carrier Modulation [J]. Computer Science, 2022, 49(3): 322-328.
[10] PAN Yan-na, FENG Xiang, YU Hui-qun. Competitive-Cooperative Coevolution for Large Scale Optimization with Computation Resource Allocation Pool [J]. Computer Science, 2022, 49(2): 182-190.
[11] WANG Ying-kai, WANG Qing-shan. Reinforcement Learning Based Energy Allocation Strategy for Multi-access Wireless Communications with Energy Harvesting [J]. Computer Science, 2021, 48(7): 333-339.
[12] SONG Hai-ning, JIAO Jian, LIU Yong. Research on Mobile Edge Computing in Expressway [J]. Computer Science, 2021, 48(6A): 383-386.
[13] SUN Yi-fan, MI Zhi-chao, WANG Hai, ZHAO Ning. Cluster-based Topology Adaptive OLSR Protocol for UAV Swarm Network [J]. Computer Science, 2021, 48(6): 268-275.
[14] WANG Cong, WEI Cheng-qiang, LI Ning, MA Wen-feng, TIAN Hui. Dynamic Allocation Mechanism of Preamble Resources Under H2H and M2M Coexistence Scenarios [J]. Computer Science, 2021, 48(5): 283-288.
[15] LI Zhen-jiang, ZHANG Xing-lin. Resource Allocation and Offloading Decision of Edge Computing for Reducing Core Network Congestion [J]. Computer Science, 2021, 48(3): 281-288.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!