Joint Optimization of Resource Allocation and FOV for VR services in Mobile Edge Networks

Wang, Ying; Liu, Man; Fei, Zixuan

doi:10.1109/iccc51575.2020.9345037

Cited by 2 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MEC servers are deployed at the edge of the network, which can improve the computational capability and data caching capability of the network, reduce user latency, and reduce network congestion [21]. Song et al [22] used mobile edge computing (MEC) to extract FOV videos from 360-degree videos to avoid transmission bandwidth occupation and backhaul link traffic between the base station and the core network. e cache placement and FOV selection of wireless VR service network are studied.…”

Section: Vr Network System Model Based On User Interest and Fovmentioning

confidence: 99%

The Design of Urban Sculpture Space with User Behavior Based on Internet of Things and Edge Computing

2022

Scientific Programming

View full text Add to dashboard Cite

To solve the application of Virtual Reality (VR) technology in the design of urban sculpture space, a VR network model is implemented based on Internet of Things (IoT) and Edge Computing. First, this work expounds and analyzes the theory of urban sculpture space and the interaction of urban sculpture space. Second, aiming at the problem that the development of IoT cannot meet the large amount of data and low delay of VR, a resource allocation algorithm, Joint Optimization of Resource Allocation and FOV (J-RAF) strategy is proposed based on the user interest and the resource allocation problem of (FOV), and simulation experiments are used to verify the effectiveness of the J-RAF algorithm. Finally, a VR network system is established based on cache cooperation and user interest, and a resource allocation algorithm Joint Cooperative Content Caching and FOV (J-CCF) is proposed, and its effectiveness is verified. Experiments show that compared with random strategy and random viewport strategy, algorithm J-CCF has 3% and 20% system gain. Compared with noncooperative cache scheme and random viewport strategy, algorithm J-CCF can improve the system gain by 15% and 25%, respectively. It is verified that the convergence of J-RAF algorithm and J-CCF algorithm and the effectiveness of improving the system gain. This work provides a technical basis for optimizing the design method of urban sculpture and contributes to the optimal design of urban space.

show abstract

Section: Vr Network System Model Based On User Interest and Fovmentioning

confidence: 99%

The Design of Urban Sculpture Space with User Behavior Based on Internet of Things and Edge Computing

2022

Scientific Programming

View full text Add to dashboard Cite

show abstract

Energy- and Quality-Aware Task Offloading for WebVR Service in Terminal-Aided Mobile Edge Network

Yang

Feng

Que

et al. 2022

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

Joint Optimization of Resource Allocation and FOV for VR services in Mobile Edge Networks

Cited by 2 publications

References 12 publications

The Design of Urban Sculpture Space with User Behavior Based on Internet of Things and Edge Computing

The Design of Urban Sculpture Space with User Behavior Based on Internet of Things and Edge Computing

Energy- and Quality-Aware Task Offloading for WebVR Service in Terminal-Aided Mobile Edge Network

Contact Info

Product

Resources

About