基于M值概率分布的网络视频流分类

杨凌云; 董育宁; 王再见; 汤萍萍

doi:10.11999/JEIT170617

基于M值概率分布的网络视频流分类

doi: 10.11999/JEIT170617

2.
(南京邮电大学通信与信息工程学院南京 210003) ②(安徽师范大学物理与电子信息学院芜湖 241000)

基金项目:

国家自然科学基金(61271233, 61401004, 61601005)，华为HIRP创新项目，安徽师范大学博士科研启动金项目(2016XJJ129)

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- 被引次数: 0
出版历程
- 收稿日期: 2017-06-28
- 修回日期: 2018-02-23
- 刊出日期: 2018-05-19

Network Video Traffic Classification Based on Probability Distribution of M Value

2.
(College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications,Nanjing 210003, China)

Funds:

The National Natural Science Foundation of China (61271233, 61401004, 61601005), The HIRP Program of Huawei Technology Co. Ltd, The Ph.D Programs Foundation of Anhui Normal University (2016XJJ129)

摘要

摘要: 为了改善网络视频流的细粒度分类效果，该文分析视频流传输过程中的特征变化与流分类之间的关系。根据不同类型的视频流具有不同的下行传输速率变化模式，提出一种新的基于下行速率传输的视频流分类特征--M值概率分布，并使用支持向量机(SVM)实现网络视频流的分类。实验结果表明，M值概率分布相比较于已有的常见流特征，可以更好地实现6种典型的网络视频流分类。
- 网络视频流 /
- 流分类 /
- M值概率分布
Abstract: To obtain better results for fine-grained video traffic classification, this paper analyzes the relationship between the feature variations during transmission and video traffic classification. According to the nature that different types of video services contain different downlink transmission rate variation patterns, a new video flow feature M value probability distribution, based on downlink byte rate variation is proposed, and video classification is realized by Support Vector Machine (SVM). The experimental results show that the probability distribution of M value is a better feature for classification of six kinds of common network video applications than other commonly used flow features.
- Network video /
- Traffic classification /
- Probability distribution of M value

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参考文献(24)

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