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Stereoscopic video gives viewers more realistic vision than traditional 2D videos by transmitting two different views simultaneously. It doubles the required bandwidth in comparison with single view videos. Motion and disparity estimation therefore play a key role in reducing the bit rate of stereoscopic videos. However, it brings extremely huge computational complexity to an encoder which obstructs it from practical uses. In the past few years, some fast algorithms were proposed where most of them speed up the coding time based on an accurate estimation in one field (motion/disparity field), and then relieve the computational burden in the other field (disparity/motion field). Nevertheless, the complexities of both motion and disparity estimation cannot be fully reduced. In this paper, an iterative motion and disparity estimation algorithm is proposed. The proposed algorithm can determine motion vectors of the right view and disparity vectors of the current stereo pair in an iterative way. The gain of this iterative search is due to the use of a stereo-motion consistency constraint in which the motion and disparity vectors can be estimated by updating the local optimal vectors iteratively. An adaptive search range adjustment through the confidence measure of the constraint is then designed to further strengthen the reliability of each step for the iterative search. Experimental results show that the speed-up gain of the proposed algorithm is 18.76 ~ 229.19 times compared to the full search algorithm with a negligible quality drop.