A Cooperative Caching and Computing-Offloading Method for 3C Trade-Off in VR Video Services

Li, Qi; Wang, Dongyu; Lü, Hua

doi:10.1109/access.2021.3110741

Cited by 8 publications

(2 citation statements)

References 25 publications

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“…In [36], authors ignore routes (4) and ( 7) compared with the proposed work. Further, in [19], they ignore the route (5), while only [35] considers all eight service routes as in this paper. However, [35] does not consider a dynamically changing environment, only providing one instance problem.…”

Section: B Vr Service Routesmentioning

confidence: 99%

Multi-Agent Federated Reinforcement Learning Strategy for Mobile Virtual Reality Delivery Networks

Liu

Garg

Ratnarajah

2024

IEEE Trans. Netw. Sci. Eng.

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Virtual reality (VR) services have become increasingly popular but presented challenges for wireless communications due to the large amounts of data requirements. In this work, we consider a dynamic changing VR scenario and propose a joint caching, computing, and communication (3C) strategy, subject to bounded latency, power, caching, and computing constraints, to minimize long-term discounted delay and energy consumption for VR projection. Our approach involves a threelayer communication system consisting of a cloud server, UAV (Unmanned Aerial Vehicle) base stations with mMIMO (massive Multiple-Input Multiple-Output) acting as edge servers, and mobile user devices. To satisfy different users' requirements, we design eight service routes for 3C decisions. We then employ federated multi-agent deep reinforcement learning (RL) to help users obtain optimal service routes influenced by their location, orientation, and content preference, with edge servers acting as learning agents. For the RL part, we design multi-input and output actor and critic networks deployed on edge servers. For the Federated Learning (FL) part, we present the federated average process and mathematically prove its convergence. Simulation results demonstrate our proposed algorithm can effectively reduce training loss, converge smoothly, and significantly reduce both delay and energy consumption by approximately 18.3% and 25.6%, respectively.

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Section: B Vr Service Routesmentioning

confidence: 99%

Multi-Agent Federated Reinforcement Learning Strategy for Mobile Virtual Reality Delivery Networks

Liu

Garg

Ratnarajah

2024

IEEE Trans. Netw. Sci. Eng.

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“…Since 2016, the virtual reality industry has been growing rapidly. But due to the high demand for local computing and rendering equipment, users are still mainly a few enthusiasts and VR business is difficult to serve ordinary users [1]. Cloud virtual reality (VR) is a cloud-based real-time virtual reality technology, which uses cloud servers instead of users' local computing devices [2].…”

Section: Introductionmentioning

confidence: 99%

A Deep Reinforcement Learning-Based Optimal Computation Offloading Scheme for VR Video Transmission in Mobile Edge Networks

Xu,

Song

2023

IEEE Access

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Large bandwidth, Low latency and intensive computing are the main challenge in high-performance virtual reality (VR) video transmission. As mobile edge computing (MEC) can provide computation and storage resources closer to terminals, it has been a promising mode in VR video transmission to substantially improve communication quality. This work focuses on the autonomous perception ability in MEC-supported VR video transmission, and introduces deep reinforcement learning to investigate optimal task offloading solutions. Therefore, this paper proposes a deep reinforcement learning-based optimal computation offloading scheme for VR video transmission in mobile edge networks. Specifically, a Deep Deterministic Policy Gradient-based computation offloading algorithm in designed as the main technical framework. The optimal planning of computation offloading strategies is viewed as a Markov decision problem, and a deep Q-Network is employed to deal with it. Finally, the setting of MEC-supported VR video transmission scenes is simulated, in which the proposed scheme is implemented for evaluation. The results are displayed in visualization format and show that the proposed task computation scheme can possess proper performance results in MEC-supported VR video transmission scenes.INDEX TERMS Virtual reality, video transmission, computation offloading, mobile edge networks, reinforcement learning.

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Physical layer signal processing for XR communications and systems

Wu,

Xu,

Zhai

et al. 2024

Sci. China Inf. Sci.

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A Cooperative Caching and Computing-Offloading Method for 3C Trade-Off in VR Video Services

Cited by 8 publications

References 25 publications

Multi-Agent Federated Reinforcement Learning Strategy for Mobile Virtual Reality Delivery Networks

Multi-Agent Federated Reinforcement Learning Strategy for Mobile Virtual Reality Delivery Networks

A Deep Reinforcement Learning-Based Optimal Computation Offloading Scheme for VR Video Transmission in Mobile Edge Networks

Physical layer signal processing for XR communications and systems

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