Title :
Link quality prediction for multimedia streaming based on available bandwidth and latency
Author :
Lim Su Jin ; Lee Sze Wei ; Lau, Simon ; Karuppiah, Ettikan
Author_Institution :
Univ. Tunku Abdul Rahman, Kuala Lumpur, Malaysia
Abstract :
Network performance metrics such as available bandwidth and latency are essential to achieve good Quality of Service (QoS) in multimedia streaming. There are unique requirements in network performance metrics for media applications, such as audio conferencing, video streaming, video conferencing, and high-definition (HD) video conferencing. In this paper, we focus on conference call type suggestion based on link quality prediction. The link´s quality is classified based on the available bandwidth and latency between two network nodes. We have implemented and compared two of the most popular supervised learning based classification methods, i.e. logistic regression and support vector machine (SVM). We have compared the performance of both methods and their suitability to apply in link quality prediction. The experimental results show that SVM outperforms logistic regression for binary and multiclass classification in terms of accuracy.
Keywords :
computer network performance evaluation; media streaming; multimedia communication; pattern classification; quality of service; radio links; regression analysis; SVM; bandwidth availability; binary classification; latency; link quality prediction; logistic regression; media applications; multiclass classification; multimedia streaming; network performance metrics; quality of service; Accuracy; Bandwidth; Kernel; Logistics; Streaming media; Support vector machines; Training; classification; logistic regression; multimedia streaming; support vector machine;
Conference_Titel :
Local Computer Networks Workshops (LCN Workshops), 2013 IEEE 38th Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
978-1-4799-0539-3
DOI :
10.1109/LCNW.2013.6758547