Communication-and-Computing Latency Minimization for UAV-Enabled Virtual Reality Delivery Systems

In this paper, we propose a low-latency virtual reality (VR) delivery system where an unmanned aerial vehicle (UAV) base station (U-BS) is deployed to deliver VR content from a cloud server to multiple ground VR users. Each VR input data requested by the VR users can be either projected at the U-BS...

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Bibliographic Details
Published in:IEEE transactions on communications Vol. 69; no. 3; pp. 1723 - 1735
Main Authors: Zhou, Yi, Pan, Cunhua, Yeoh, Phee Lep, Wang, Kezhi, Elkashlan, Maged, Vucetic, Branka, Li, Yonghui
Format: Journal Article
Language:English
Published: New York IEEE 01.03.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Bandwidths
Caching
Cloud computing
Clouds
computing
Iterative algorithms
Iterative methods
joint optimization
latency minimization
Minimization
Resource management
Servers
Telecommunication computing
UAV communication
Unmanned aerial vehicles
Virtual reality
ISSN:0090-6778, 1558-0857
Online Access:Get full text
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Summary:In this paper, we propose a low-latency virtual reality (VR) delivery system where an unmanned aerial vehicle (UAV) base station (U-BS) is deployed to deliver VR content from a cloud server to multiple ground VR users. Each VR input data requested by the VR users can be either projected at the U-BS before transmission or processed locally at each user. Popular VR input data is cached at the U-BS to further reduce backhaul latency from the cloud server. For this system, we design a low-complexity iterative algorithm to minimize the maximum communications and computing latency among all VR users subject to the computing, caching and transmit power constraints, which is guaranteed to converge. Numerical results indicate that our proposed algorithm can achieve a lower latency compared to other benchmark schemes. Moreover, we observe that the maximum latency mainly comes from communication latency when the bandwidth resource is limited, while it is dominated by computing latency when computing capacity is low. In addition, we find that caching is helpful to reduce latency.
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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2020.3040283