Comprehensive survey on resource allocation for edge-computing-enabled metaverse

With the rapid evaluation of virtual and augmented reality, massive Internet of Things networks and upcoming 6 G communication give rise to an emerging concept termed the "metaverse," which promises to revolutionize how we interact with the digital world by offering immersive experiences between reality and virtuality. Edge computing, another novel paradigm, propels the metaverse functionality by enhancing real-time interaction and reducing latency, providing a responsive and seamless virtual environment. However, realizing the full potential of the metaverse requires dynamic and efficient resource-allocation strategies to handle the immense demand for communicational, computational, and storage resources required by its diverse applications. This survey comprehensively explores resource-allocation strategies in the context of an edge-computing-enabled metaverse, investigating various challenges, existing techniques, and emerging trends in this rapidly expanding field. We first explore the underlying metaverse characteristics and pivotal role of edge computing, after which we investigate various types of resources and their key issues and challenges. We also provide a brief discussion on offloading and caching strategies, which are the most prominent research issues in this context. In this study, we compare and analyze 35 different resource-allocation strategies, benchmark 19 algorithms, and investigate their suitability across diverse metaverse scenarios, offering a broader scope than existing surveys. The survey aims to serve as a comprehensive guide for researchers and practitioners, helping them navigate the complexities of resource allocation in the metaverse and supporting the development of more efficient, scalable, and user-centric virtual environments.