SPECTS 2007 START Conference Manager    

Feature Selection for Prediction of User-Perceived Streaming Media Quality

Amy Csizmar Dalal and Jamie Olson

International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS 2007)
San Diego, California (USA), July 16-18, 2007


This paper considers the selection of features, measurements collected from an instrumented media player application, that most accurately predict the user-perceived quality of a media stream. The features are utilized by a nearest-neighbor stream quality prediction algorithm using a distance metric of dynamic time warping. We explore three ways of selecting features from this data: manually, by observing how application-layer measurements change with changing network congestion conditions; correlation-based; and a mathematically-based technique using principal component analysis (PCA). We compare the prediction algorithm's accuracy obtained using the features selected by each method, using a performance evaluation metric we term hit rate. Our results show that each method selects one feature set that, when used by our predictor, yields very high hit rates (typically 70-90%), and that each of these feature sets includes one particular feature in common: retransmitted packets. We also show that the correlation-based and PCA-based methods of selecting features do not consistently select acceptable feature sets for our stream quality predictor, in terms of the hit rates generated by the predictor.

START Conference Manager (V2.54.3)
Maintainer: sbranch@scs.org