Progressive scene transmission in massive distributed virtual environments based on a two-layer P2P architecture with scalable multi-level regions of interest

This paper presents a progressive scene transmission framework for massive distributed virtual environments based on a scalable two-layer P2P architecture with multi-level regions of interest (ROI). The proposed design integrates global coordination by super-peers and localised diffusion by swarm peers to achieve adaptive, perceptually prioritised transmission. A cognition-driven ROI model and predictive scaling mechanism enhance responsiveness and stability under heterogeneous network conditions. Experimental results demonstrate substantial improvements in latency, coverage, and consistency compared with classical P2P and centralised methods, confirming the efficiency and robustness of the proposed architecture for large-scale interactive systems.