Based on the empirical envelope of a compressed video stream, a traffic description model was developed.
以视频码流流量的实测包络为基础，提出了一种流量描述模型。
Modeling video traffic is an important prerequisite for any network performance simulating, but it is very difficult to build an uniform model for video traffic since video contents are variable.
视频业务流量模型是网络性能仿真的一个重要前置环节，但由于视频内容的千差万别，使得很难建立统一的视频业务流量模型。
It is the important method to realize the higher precision prediction and improve the utilizing rate. A support vector machine model is proposed to predict the VBR video traffic.
实现该信号通信量的高精度预测是提高信息传输速度和提高网络带宽资源利用效率的重要手段，本文提出采用支持向量机网络模型对VBR视频通信量进行预测。
Moreover, this paper implemented a video traffic generator embedded in NS-2 according to the model proposed in this paper. And it is applied to network simulation.
另外，根据本文提出的视频流量模型实现了一个内嵌于ns - 2的视频流量产生器，并用它进行了网络仿真验证。
Based on the MPEG-4 video traffic of low quality and high quality, a hybrid model is presented to capture the frame size at multiple time scales: the change of scene and fluctuation of bit-rate.
针对MPEG - 4的低质量和高质量两种编码质量的视频源，给出了一种混合模型，此模型在多个时间尺度上反映帧大小的变化：场景的变化和同一场景内码流的波动。
For the frequently scene switching video traffic, it USES Markovian to simulate the switching among the scenes, and USES self-regression model to modeling inside the scene.
针对具有频繁场景切换的视频流，用马尔可夫链来模拟视频场景之间的切换，场景内部用自回归模型来建模。
For the frequently scene switching video traffic, it USES Markovian to simulate the switching among the scenes, and USES self-regression model to modeling inside the scene.
针对具有频繁场景切换的视频流，用马尔可夫链来模拟视频场景之间的切换，场景内部用自回归模型来建模。
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