多媒體網路上媒體平滑播放技術

Abstract

在多媒體網路上傳輸影片及語音，會因為網路中的jitter問題導至播放時影像不平順和語音難以分辨的現象。因此，便需要借由媒體平滑播放器(intra-media synchronization)來消除jitter，而同時還能保持可接受的播放throughput以及盡可能小的延遲。在這論文中，我們提出了駕構於網路接收端的兩個媒體平滑播放機制，分別針對傳輸中的影片和語音作用: Intelligent Video Smoother (IVS)及Intelligent Voice Smoother (IVoS)。IVS包含了一個Neural Network (NN) 訊務預測器，一個NN window determinator，以及一個window-based 平滑播放演算法。其中，NN訊務預測器利用了一個on-line trained back propagation neural network (BPNN)定時的預測未來固定期間的訊務特質參數，然後交給NN window determinator來決定最佳的window (以能達到最小playout Quality值為標準)，之後window-based平滑播放演算法便根據所得到的window以及當時在緩衝器內的packet量等資訊，動態的改變影片播放的速率。最後，我們跟據模擬的結果發現，相對於其他兩種不同的撥放方法，IVS展現了最高的throughput以及最小的discontinulity。 而IVoS則包含了三個主要的部份:Smoother Buffer，NN訊務預測器，以及Constant Bit Rate (CBR) Enforcer。任一個被假設具有Markov Modulated Bernoulli Process (MMBP)的packet會先在Smoother Buffer暫存，然後NN訊務預測器利用on-line trained BPNN對未來的訊務進行預測，接著CBR Enforcer便根據所得到的預測資訊動態的調整talkspurt的第一個packet的buffering delay，以使得播放的Distortion of Talkspurts (DOT)和Playout Delay (PD)之平均及變異量達到最小的程度。經由模擬實驗的結果發現，相對於其他的方法，IVoS在任何的訊務狀態下，皆得到較佳的DOT及PD的要求。

For transporting vedio and voice data over the network, the problem of jitter introduced from the network often renders the playout discontinuity and speech unintelligible. It is thus indispensable to offer intramedia synchronization to remove jitter while retaining satisfactory playout throughput and minimal playout delay. In this thesis, we propose two smoothing mechanisms, operating at the application layer of the receiving end system, for video and voice communications: Intelligent Vedio Smoother(IVS) and Intelligent Voice Smoother (IVoS). 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 Back Propagation 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 that, via simulation results and live video scenes, compared to two other playout approaches, IVS achieves high-throughput and low-discontinuity playout under a mixture of IBP arrivals. IVoS is composed of three components: Smoother Buffer, Neural Network (NN) Traffic Predictor, and Constant Bit Rate (CBR) Enforcer. Newly arriving frames, being assumed to follow a generic Markov Modulated Bernoulli Process (MMBP), are queued in the Smoother Buffer. The NN Traffic Predictor employs an on-line-trained Back Propagation Neural Network (BPNN) to predict three traffic characteristics of every newly encountered talkspurt period. Based on the predicted characteristics, the CBR Enforcer derives an adaptive buffering delay by means of a near-optimal, simple, closed-form formula. It then imposes such delay on the playout of the first frame in the talkspurt period. The CBR Enforcer in turn regulates CBR-based departures for the remaining frames of the talkspurt, aiming at assuring minimal mean and variance of Distortion of Talkspurts (DOT) and mean Playout Delay (PD). Simulation results reveal that, compared to three other playout approaches, IVoS achieves superior playout yielding negligible DOT and PD irrespective of traffic variation.