Full Text:   <1355>

CLC number: TN919.8

On-line Access: 

Received: 2005-12-04

Revision Accepted: 2006-02-15

Crosschecked: 0000-00-00

Cited: 0

Clicked: 3365

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE A 2006 Vol.7 No.5 P.794~800


A network condition classification scheme for supporting video delivery over wireless Internet

Author(s):  Chan Siu-ping, Sun Ming-ting

Affiliation(s):  Department of Electrical Engineering, University of Washington, Seattle 98195, USA

Corresponding email(s):   spchan@ee.washington.edu, sun@ee.washington.edu

Key Words:  Video transport, End-to-end QoS, Wireless Internet, Network condition classification, Support Vector Machine (SVM)

Chan Siu-ping, Sun Ming-ting. A network condition classification scheme for supporting video delivery over wireless Internet[J]. Journal of Zhejiang University Science A, 2006, 7(5): 794~800.

@article{title="A network condition classification scheme for supporting video delivery over wireless Internet",
author="Chan Siu-ping, Sun Ming-ting",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T A network condition classification scheme for supporting video delivery over wireless Internet
%A Chan Siu-ping
%A Sun Ming-ting
%J Journal of Zhejiang University SCIENCE A
%V 7
%N 5
%P 794~800
%@ 1673-565X
%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A0794

T1 - A network condition classification scheme for supporting video delivery over wireless Internet
A1 - Chan Siu-ping
A1 - Sun Ming-ting
J0 - Journal of Zhejiang University Science A
VL - 7
IS - 5
SP - 794
EP - 800
%@ 1673-565X
Y1 - 2006
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2006.A0794

Real-time video transport over wireless Internet faces many challenges due to the heterogeneous environment including wireline and wireless networks. A robust network condition classification algorithm using multiple end-to-end metrics and support Vector Machine (SVM) is proposed to classify different network events and model the transition pattern of network conditions. End-to-end Quality-of-Service (QoS) mechanisms like congestion control, error control, and power control can benefit from the network condition information and react to different network situations appropriately. The proposed network condition classification algorithm uses SVM as a classifier to cluster different end-to-end metrics such as end-to-end delay, delay jitter, throughput and packet loss-rate for the UDP traffic with TCP-friendly Rate Control (TFRC), which is used for video transport. The algorithm is also flexible for classifying different numbers of states representing different levels of network events such as wireline congestion and wireless channel loss. Simulation results using network simulator 2 (ns2) showed the effectiveness of the proposed scheme.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1] Biaz, S., Vaidya, N., 1999. Discriminating Congestion Losses from Wireless Losses Using Interarrival Times at the Receiver. Proc. of IEEE Symposium Application—Specific Systems and Software Engineering and Technology. Richardson, TX, p.10-17.

[2] Boser, B.E., Guyon, I.M., Vapnik, V., 1992. A Training Algorithm for Optimal Margin Classifiers. Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory. ACM Press.

[3] Cen, S., Cosman, P.C., Voelkar, G.M., 2003. End-to-end differentiation of congestion and wireless loss. IEEE/ ACM Transactions on Networking, 11(5):703-717.

[4] Floyd, S., Handley, M., Padhye, J., Widmer, J., 2000. Equation-Based Congestion Control for Unicast Applications. Proc. of ACM SIGCOMM, p.43-56.

[5] Fu, Z., Meng, X., Lu, S., 2003. A transport protocol for supporting multimedia streaming in mobile ad hoc networks. IEEE Journal on Selected Areas in Communications, 21(10):1615-1626.

[6] Kurose, J.F., Ross, K.W., 2003. Computer Networking: A Top-Down Approach Featuring the Internet, 2nd Ed. Addison Wesley.

[7] Liu, J., Matta, I., Crovella, M., 2003. End-to-end Inference of Loss Nature in Hybrid Wired/Wireless Environment. Proc. of WiOpt’03: Modeling and Optimization in Mobile, Ad hoc and Wireless Networks.

[8] Puri, R., Ramchandran, K., Ortega, A., 1998. Joint Source Channel Coding with Hybrid ARQ/FEC for Robust Video Transmission. Proc. IEEE Multimedia Signal Processing Workshop. Redondo Beach, CA.

[9] Tobe, Y., Tamura, Y., Molano, A., Ghosh, S., Tokuda, H., 2000. Achieving Moderate Fairness for UDP Flows by Path-status Classification. Proc. of IEEE LCN. Tampa, FL, p.252-261.

[10] Vapnik, V., 1998. Statistical Learning Theory. Wiley, New York.

[11] Wu, D., Hou, Y.T., Zhang, Y.Q., 2001. Scalable video coding and transport over broadband wireless networks. Proceeding of the IEEE, 89(1):6-20.

[12] Zhang, Q., Kassam, S.A., 1999. Hybrid ARQ with selective combining for fading channels. IEEE Journal on Selected Areas in Communications, 17(5):867-880.

[13] Zhang, Q., Zhu, W., Zhang, Y.Q., 2004. Channel-adaptive resource allocation for scalable video transmission over 3G wireless network. IEEE Transactions on Circuit and Systems for Video Technology, 14(8):1049-1063.

[14] Zhang, Q., Zhu, W., Zhang, Y.Q., 2005. End-to-end QoS for video delivery over wireless Internet. Proceedings of the IEEE, 93(1):123-134.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE