论文标题
用于带宽估算的条件概率分布模型，并在实时视频流中应用
A Conditional-Probability-Distribution Model for Bandwidth Estimation with Application in Live Video Streaming
论文作者
论文摘要
如果视频上传器对未来可用的带宽有更准确的了解，则可以改善实时视频流的经验。因为有了这样的知识，人们就可以知道他应该在不断变化的网络中编码框架的尺寸。研究人员已经开发了一些算法来预测文献中的吞吐量，因此有些是简单的。但是，限制仍然是大多数当前带宽预测方法正在预测未来带宽的值或点估计值。因为在许多实际情况下，希望控制某些目标的性能，例如，视频传递速率超过给定目标百分比，这是无法通过大多数当前方法轻松实现的。 在这项工作中，我们建议使用概率分布来对未来的带宽进行建模。具体而言，我们使用过去的数据传输测量值对未来的带宽进行建模，然后得出用于应用程序中的概率模型。这将应用程序中的参数选择变为概率方式，从长远来看可以实现给定的目标性能。在我们的模型中，我们使用条件概率的方法将过去和将来的带宽相关联，从而进一步改善了估计性能。
Experience of live video streaming can be improved if the video uploader has more accurate knowledge about the future available bandwidth. Because with such knowledge, one is able to know what sizes should he encode the frames to be in an ever-changing network. Researchers have developed some algorithms to predict throughputs in the literature, from where some are simple hence practical. However, limitation remains as most current bandwidth prediction methods are predicting a value, or a point estimate, of future bandwidth. Because in many practical scenarios, it is desirable to control the performance to some targets, e.g., video delivery rate over a given target percentage, which cannot be easily achieved via most current methods. In this work, we propose the use of probability distribution to model future bandwidth. Specifically, we model future bandwidth using past data transfer measurements and then derive a probability model for use in the application. This changes the selection of parameters in application into a probabilistic manner such that given target performance can be achieved in the long run. Inside our model, we use the conditional-probability method to correlate past and future bandwidth and hence further improve the estimating performance.
