Neyman-Pearson Criterion Driven NFV-SDN Architectures and Optimal Resource-Allocations for Statistical-QoS Based mURLLC Over Next- Generation Metaverse Mobile Networks Using FBC

Metaverse streaming, as one of the key wireless services over 6G mobile networks, generates the delay/error-sensitive and bandwidth-intensive wireless traffics with stringent quality-of-service (QoS) requirements. Consequently, metaverse streaming can be modeled as a new type of massive ultra-reliable low-latency communications (mURLLC) traffic over 6G mobile networks. However, how to efficiently support metaverse streaming with constrained wireless resources and dynamic network conditions has imposed many new challenges not encountered before. To conquer these difficulties, in this paper we propose the Neyman-Pearson criterion driven network functions virtualization (NFV) and software-defined network (SDN) architectures and optimal resource-allocations for statistical-QoS theory based mURLLC streaming over 6G metaverse mobile networks using finite blocklength coding (FBC). First, we use Neyman-Pearson hypothesis tests for characterizing metaverse streaming requests' distribution profiles to predict their future accessing frequencies/patterns. Second, our formulated NFV/SDN architectures and virtual-network slices are assigned to the designated metaverse mobile users with the same predicted data request distributions, categories, and statistical-QoS requirements. Third, integrating the statistical QoS theory with FBC, we develop metaverse-streaming schemes by maximizing aggregate ϵ-effective capacity and deriving optimal transmit power allocations. Finally, we use numerical analyses to validate and evaluate our proposed schemes over 6G mobile networks.