Intelligent video smoother for multimedia communications

Multimedia communications often require intra-media synchronization for video data to prevent potential playout discontinuity resulting from network delay variation while still achieving satisfactory playout throughput. We propose a neural-network-based intra-video synchronization mechanism, called the intelligent video smoother (IVS), operating at the application layer of the receiving end system. The IVS 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 the characteristics of traffic modelled by a generic interrupted Bernoulli process (IBP) over a future fixed time period. 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. Finally, we show via simulation results that, compared to two other playout approaches, the IVS achieves a high-throughput and low-discontinuity playout under a mixture of IBP arrivals