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 on-line-trained Back Propagation 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 (Q) 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.
|出版狀態||Published - 1 十二月 1996|
|事件||Proceedings of the IEEE International Conference on Industrial Technology - Shanghai, China|
持續時間: 5 十二月 1994 → 9 十二月 1994
|Conference||Proceedings of the IEEE International Conference on Industrial Technology|
|期間||5/12/94 → 9/12/94|