Intra-media synchronization for multimedia communications

Multimedia communications require intra-media synchronization for video data to prevent potential playout discontinuity resulting from network delay variation (jitter) while still achieving satisfactory playout throughput. In this paper, we propose a neural-network-based intra-media synchronization mechanism, called neural network smoother (NNS). NNS is composed of a neural network (NN) traffic predictor, an NN window determinator, and a window-based playout smoothing algorithm. The NN traffic predictor employs an online-trained backpropagation neural network (BPNN) to periodically predict future traffic characteristics. The NN window determinator determines the corresponding optimal window by means of an off-line-trained BPNN in an effort to achieve a maximum of the playout quality value. According to the window, the window-based playout smoothing algorithm then dynamically adopts various playout rates. Compared to two other playout approaches, simulation results show that NNS achieves high-throughput and low-discontinuity playout under a variety of traffic arrivals