LAUSR

Wireless virtual reality (VR)-enabled medical treatment (WVMT) system, integrating the VR technology and the platform of the Internet of Medical Things (IoMT), is a promising application in future medical industries. Multiaccess edge computing (MEC) is an effective approach to support the ubiquitous applications of WVMT systems. Due to the high requirements of medical services, the computation efficiency and security are two issues in WVMT systems. In this article, we propose a blockchain-enabled task offloading scheme, where the viewport rendering tasks of VR devices (VDs) can be offloaded to edge access points (EAPs). The blockchain is integrated into the system to reach the consensus of the global information of task offloading and data processing to resist malicious attacks. To reduce VDs’ computation load under the promise of high VR QoE, we formulate the computation offloading and resource allocation to be a Markov decision problem, considering block consensus, content correlation, and fluctuating channel conditions. Then, a novel collective reinforcement learning (CRL) algorithm is proposed to adaptively allocate resources based on the requirements of viewport rendering, block consensus, and content transmission. In the simulations, the convergence rate and the performance in terms of energy consumption and stalling rate are evaluated. simulation results demonstrate the effectiveness of the proposed scheme.