面向移动网络的视频初始缓冲队列长度测量方法

程光; 房敏; 吴桦

doi:10.11959/j.issn.1000-436x.2019179

您当前的位置：

首页 >

文章列表页 >

面向移动网络的视频初始缓冲队列长度测量方法

学术论文 | 更新时间：2024-06-05

- 面向移动网络的视频初始缓冲队列长度测量方法
- Measurement of video initial buffer size for mobile network
- 通信学报 2019年40卷第10期页码：67-78
- 作者机构：
  
  1. 东南大学网络空间安全学院，江苏南京 211189
  2. 东南大学计算机网络和信息集成教育部重点实验室，江苏南京 211189
  3. 东南大学计算机科学与工程学院，江苏南京 211189
- 作者简介：
  
  [ "程光（1973– ），男，安徽黄山人，博士，东南大学教授、博士生导师，主要研究方向为网络空间安全监测和防护、网络大数据分析。" ]
  [ "房敏（1989– ），男，山东泰安人，东南大学硕士生，主要研究方向为网络测量和网络安全。" ]
  [ "吴桦（1973– ），女，江苏南京人，博士，东南大学副教授，主要研究方向为网络测量、网络安全和网络管理。" ]
- 基金信息：
  
  江苏省研究生科研与实践创新计划基金资助项目(SJCX17_0021);国家重点研发计划基金资助项目(2017YFB0801703);国家自然科学基金资助项目(61602114);国家自然科学基金资助项目(61872080);国家自然科学基金资助项目(61502098);江苏省自然科学基金资助项目(BK20151416);赛尔网络下一代互联网技术创新基金资助项目(NGII20170406)
- DOI：10.11959/j.issn.1000-436x.2019179
  中图分类号： TP391.41
- 网络出版日期：2019-10，
  
  纸质出版日期：2019-10-25
- 稿件说明：
移动端阅览
程光, 房敏, 吴桦. 面向移动网络的视频初始缓冲队列长度测量方法[J]. 通信学报, 2019,40(10):67-78.

Guang CHENG, Min FANG, Hua WU. Measurement of video initial buffer size for mobile network[J]. Journal on communications, 2019, 40(10): 67-78.
程光, 房敏, 吴桦. 面向移动网络的视频初始缓冲队列长度测量方法[J]. 通信学报, 2019,40(10):67-78. DOI： 10.11959/j.issn.1000-436x.2019179.

Guang CHENG, Min FANG, Hua WU. Measurement of video initial buffer size for mobile network[J]. Journal on communications, 2019, 40(10): 67-78. DOI： 10.11959/j.issn.1000-436x.2019179.

摘要

针对视频初始缓冲队列长度难以准确测量的问题，对非加密的优酷和加密的YouTube两类视频平台进行研究，提出了视频初始缓冲队列长度测量方法。通过识别分析视频流量特征，关联流量行为与播放状态，构建视频指纹库，实现了队列长度的准确测量。实验结果表明，两类视频平台的测量结果均能够精确到帧，完全满足准确评估视频体验质量的需要。

Abstract

To resolve the difficulty in accurately measure the length of video initial buffering queue

two video platforms

non-encrypted Youku and encrypted YouTube

were selected to research

and the video initial buffer queue length measurement method was proposed.By identifying and analyzing the characteristics of video traffic

correlating the traffic behavior with the playing state

constructing video fingerprint database

accurate measurement of queue length was realized.The experimental results show that the measurement results of the two types could be accurate to the frame

fully meeting the need to accurately evaluate the quality of the video experience.

关键词

Keywords

references

SANDVINE . 2018 Global internet phenomena report [R ] . [S.l.:s.n.] , 2018 .

CISCO . Cisco visual networking index:forecast and methodology 2017—2022 [R ] . San Francisco:Cisco , 2018 .

WAMSER F , CASAS P , SEUFERT M , et al . Modeling the YouTube stack:from packets to quality of experience [J ] . Computer Networks , 2016 ( 109 ): 211 - 224 .

SEUFERT M , WEHNER N , WAMSER F , et al . Unsupervised QoE field study for mobile YouTube video streaming with YoMoApp [C ] // The 9th International Conference on Quality of Multimedia Experience . IEEE , 2017 : 1 - 6 .

WAMSER F , HÖFNER S , SEUFERT M , , et al . Server and content selection for MPEG DASH video streaming with client information [C ] // The Workshop on QoE-based Analysis and Management of Data Communication Networks . ACM , 2017 : 19 - 24 .

ORSOLIC I , SUZNJEVIC M SKORIN–KAPOV L . YouTube QoE estimation from encrypted traffic:comparison of test methodologies and machine learning based models [C ] // The 10th International Conference on Quality of Multimedia Experience . IEEE , 2018 : 1 - 6 .

HOßFELD T , EGGER S , SCHATZ R , et al . Initial delay vs.interruptions:between the devil and the deep blue sea [C ] // The 4th Fourth International Workshop on Quality of Multimedia Experience . IEEE , 2012 : 1 - 6 .

HOßFELD T , SCHATZ R , KRIEGER U R . QoE of YouTube video streaming for current Internet transport protocols [M ] // Heidelberg:Springer , 2014 : 136 - 150 .

HOßFELD T , VARELA M , HEEGAARD P E , et al . QoE analysis of the setup of different internet services for FIFO server systems [C ] // International Conference on Measurement,Modelling and Evaluation of Computing Systems . Springer , 2018 : 234 - 248 .

华为技术有限公司 . 固定视频指标体系白皮书 [R ] .(2017-07-10)[2019–01–09 ] .

Huawei Technolgies Co.,Ltd . Fixed video indicators system white paper [R ] .

NUNES R D , PEREIRA C H , ROSA R L , et al . Real-time evaluation of speech quality in mobile communication services [C ] // IEEE International Conference on Consumer Electronics . IEEE , 2016 : 389 - 390 .

AMEIGEIRAS P , RAMOS‐MUNOZ J J , NAVARRO‐ORTIZ J , et al . Analysis and modelling of YouTube traffic [J ] . Transactions on Emerging Telecommunications Technologies , 2012 , 23 ( 4 ): 360 - 377 .

SCHATZ R , HOßFELD T , CASAS P . Passive youtube QoE monitoring for ISPs [C ] // Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing . IEEE , 2012 : 358 - 364 .

BURGER V , ZINNER T,DINH-XUAN L , et al . A generic approach to video buffer modeling using discrete-time analysis [J ] . ACM Transactions on Multimedia Computing,Communications,and Applications , 2018 , 14 ( 2s ):33.

KRISHNAMOORTHI V , CARLSSON N , HALEPOVIC E , et al . BUFFEST:predicting buffer conditions and real-time requirements of HTTP (S) adaptive streaming clients [C ] // The 8th ACM on Multimedia Systems Conference . ACM , 2017 : 76 - 87 .

TSILIMANTOS D , KARAGKIOULES T , VALENTIN S . Classifying flows and buffer state for YouTube's HTTP adaptive streaming service in mobile networks [C ] // The 9th ACM Multimedia Systems Conference . ACM , 2018 : 138 - 149 .

浏览量

840

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

移动设备加密流量的用户信息探测研究展望