3-D physical motion-based bandwidth prediction for video conferencing

Interactive video services such as video conferencing, distance learning, and online video games over the Internet and wireless networks are becoming increasingly prevalent. Because of limited bandwidth on networks and the bandwidth-hungry nature of video, interactive video requires extremely efficient resource management to gain real-time performance. Unlike pregenerated video whose traffic profiles can be computed in advance, efficiency and accuracy of dynamic resource allocation methods for interactive video depend critically on the performance of traffic prediction. Using either traffic data or image features, existing traffic prediction schemes can only provide a short-term traffic prediction. Based on a three-dimensional object's motion, this paper presents a new bandwidth prediction approach for video conferencing. We show that there is a strong correlation between video conferencing traffic and real motion of objects. The real motion can be predicted by the powerful technique Kalman filtering, and the estimated motion is used to make a long-term bandwidth prediction. The new traffic prediction model is tested and compared with the frame-based adaptive normalized least mean square error linear predictor and optical flow-based method with Kalman filtering. Experimental results show that our proposed traffic prediction model achieves much higher accuracy in long-term traffic prediction, which provides the possibility for networks to allocate resources efficiently for video conferencing services.