Predict-and-Quantize Priority in Application-Level Multicast for Distributed Interactive Applications

In Distributed Interactive Applications (DIAs) embedded priority, the sender node forwards data to receivers due to their respective priorities. One application level multicast protocol: the priority-based directed minimum spanning tree (PST) was designed for these DIAs. However, the existing PST has no efficient priority selection mechanism and the system consumes a tremendous amount of resource and becomes unstable and unscalable when its size increases. In this paper we propose a novel application level multicast algorithm: Predict-and-Quantize for Priority with directed minimum Spanning Tree (PQPST), which can efficiently predict priorities for the receivers and quantize the predicted priorities to build a multicast distribution tree. Furthermore, PQPST can easily integrate the quantized priority into game environment and simplify priority deployment. The simulation results show that PQPST is able to efficiently make priority selection and keep system stable with reasonable scalability.