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. 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 on-line-trained Back Propagation 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.
|頁（從 - 到）||69-76|
|期刊||Conference on Local Computer Networks|
|出版狀態||Published - 1 十二月 1996|
|事件||Proceedings of the 1996 21st Conference on Local Computer Networks - Minneapolis, MN, USA|
持續時間: 13 十月 1996 → 16 十月 1996