The incorporation of wavelet-based multiscale image decomposition in motion-estimation schemes has been shown to have a favourable impact on accuracy in tracking motion of the carotid artery wall from B-mode ultrasound image sequences. In this work, in an attempt to further enhance accuracy, we investigate the effects of different parameters of multiscale image decomposition. To this end, we optimize multiscale weighted least-squares optical flow (MWLSOF), a previously presented multiscale motion estimator, in terms of (a) the type of wavelet transform (WT) (discrete (DWT) and stationary (SWT) WTs), (b) the WT function and (c) the total number of levels of image decomposition. The optimization is performed in the context of an in silico data framework, consisting of simulated ultrasound image sequences of the carotid artery. We propose SWT, a high-order coiflet function (ex. coif5) and one level of multiscale image decomposition as the optimal parameterization for MWLSOF to achieve maximum accuracy in the particular application. Finally, we demonstrate the usefulness of an accurate motion estimator in real data experiments, by applying the optimized MWLSOF to real image data of patients with carotid atherosclerosis.