Packet Voice Rate Adaptation Through Perceptual Frame Discarding

We address the problem of rate adaptation at the source, given a congested packet based voice carrying network. We propose and analyze a novel method for perceptually based frame discarding. Thus, we propose a perceptually based classifier (PBC) to do the discarding and we combine the PBC with a method for state synchronization, which exploits the knowledge of frame discards to combat the error propagation normally following a frame loss for state dependent coders. We further propose to use a combination of a queue model and an empirical speech quality model to decide on the proper discard rate for a given congestion scenario. In this design, we particularly focus on the queue model´s ability to capture the characteristics of bursty traffic from voice sources in discontinuous transmission mode. Frame discarding is evaluated in a network bottleneck scenario, where objective results, based on PESQ-LQO, show significant improvements due to perceptually based frame discarding. The improvement depends on the degree of congestion without rate adaptation, the number of incoming voice flows, and the bottleneck buffer.