Adaptive audio streaming in mobile ad hoc networks using neural networks

We design a transport protocol that uses artificial neural networks (ANNs) to adapt the audio transmission rate to changing conditions in a mobile ad hoc network. The response variables of throughput, end-to-end delay, and jitter are examined. For each, statistically significant factors and interactions are identified and used in the ANN design. The efficacy of different ANN topologies are evaluated for their predictive accuracy. The Audio Rate Cognition (ARC) protocol incorporates the ANN topology that appears to be the most effective into the end-points of a (multi-hop) flow, using it to adapt its transmission rate. Compared to competing protocols for media streaming, ARC achieves a significant reduction in packet loss and increased goodput while satisfying the requirements of end-to-end delay and jitter. While the average throughput of ARC is less than that of TFRC, its average goodput is much higher. As a result, ARC transmits higher quality audio, minimizing root mean square and Itakura–Saito spectral distances, as well as several parametric distance measures. In particular, ARC minimizes linear predictive coding cepstral (sic) distance, which closely correlates to subjective audio measures.