Intelligent Edge Caching for Metaverse Applications Based on Grover Search

Metaverse is envisaged as an evolving Internet paradigm that allows people to play, work, and socialize in a shared and virtual ecosystem with immersive and seamless experiences. However, multiple users simultaneously request massive resources for avatar physics emulation and graphical rendering. How to provide high-quality and low-latency meta-verse services for massive concurrent users is a crucial problem. In this work, an intelligent Grover search-based edge caching (GEC) algorithm is proposed for metaverse applications, where quantum theory is used to generate caching solutions with low time complexity. Metaverse scenes can be divided into massive environment panoramic frames and dynamic objects. An edge server is deployed to render and cache the common environment panoramic frames, while the dynamic objects are rendered on head-mounted displays (HMDs). Based on the quantum theory, the panoramic frames are split into several tiles and represented by qubits. The optimal caching results are obtained by performing unitary transformations for the tiles’ features. Finally, we provide extensive simulation experiments by a real- world metaverse dataset. The numerical results reveal that the GEC algorithm can reduce 11.09% of the service time and increase 13.34% of the cache hit rate on average by comparing it with two benchmark caching algorithms.