Feature Selection for Prediction of User-Perceived Streaming Media Quality
Amy Csizmar Dalal, J. Olson · 2007
media 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 dy-namic time warping. We explore three ways of selecting fea-tures from this data: manually, by observing how application-layer measurements change with changing network conges-tion conditions; correlation-based; and a mathematically-based technique using principal component analysis (PCA). We compare the prediction algorithm’s accuracy obtained us-ing the features selected by each method, using a perfor-mance 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 fea-ture in common: retransmitted packets. We also show that the correlation-based and PCA-based methods of selecting fea-tures do not consistently select acceptable feature sets for our stream quality predictor, in terms of the hit rates generated by the predictor. 1.