A novel intra-media synchronization mechanism for multimedia communications

Multimedia communications often require intra-media synchronization for video data to prevent potential playout discontinuity resulting from network delay variation (jitter) while still achieving satisfactory playout throughput. We propose a neural-network-based intra-media synchronization mechanism, called neural network smoother (NNS). The 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 on-line-trained backpropagation neural network (BPNN) to periodically predict future traffic characteristics. With the predicted 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 (Q) value. The window-based playout smoothing algorithm then dynamically adopts various playout rates according to the window and the number of packets in the buffer. Compared to two other playout approaches, simulation results show that NNS achieves high-throughput and low-discontinuity playout under a variety of traffic arrivals