TY - GEN
T1 - Intelligent packet shaper to avoid network congestion for improved streaming video quality at clients
AU - Kaul, M.
AU - Khosla, R.
AU - Mitsukura, Y.
N1 - Publisher Copyright:
© 2003 IEEE.
PY - 2003
Y1 - 2003
N2 - This paper proposes a traffic shaping algorithm based on neural networks, which adapts to a network over which streaming video is being transmitted. The purpose of this intelligent shaper being to eradicate all traffic congestion and improve the end-users video quality. It possesses the capability to predict, to a very high level of accuracy, a state of congestion based upon the training data collected about the networks behavior. Initially the current traffic shaping technologies are discussed and later a simulation in a controlled environment is illustrated to exhibit the - effects of this intelligent traffic-shaping algorithm on the underlying network's real time packet traffic and the eradication of unwanted abruptions in the streaming videos quality. This paper concluded from the end result's of the simulation that neural networks are a very superior means of modeling real-time traffic and that it can be applied as an appropriate solution to network congestion.
AB - This paper proposes a traffic shaping algorithm based on neural networks, which adapts to a network over which streaming video is being transmitted. The purpose of this intelligent shaper being to eradicate all traffic congestion and improve the end-users video quality. It possesses the capability to predict, to a very high level of accuracy, a state of congestion based upon the training data collected about the networks behavior. Initially the current traffic shaping technologies are discussed and later a simulation in a controlled environment is illustrated to exhibit the - effects of this intelligent traffic-shaping algorithm on the underlying network's real time packet traffic and the eradication of unwanted abruptions in the streaming videos quality. This paper concluded from the end result's of the simulation that neural networks are a very superior means of modeling real-time traffic and that it can be applied as an appropriate solution to network congestion.
UR - http://www.scopus.com/inward/record.url?scp=26944448699&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=26944448699&partnerID=8YFLogxK
U2 - 10.1109/CIRA.2003.1222314
DO - 10.1109/CIRA.2003.1222314
M3 - Conference contribution
AN - SCOPUS:26944448699
T3 - Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA
SP - 988
EP - 993
BT - Proceedings - 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2003
Y2 - 16 July 2003 through 20 July 2003
ER -
