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　similar　to　229.19　times　compared　to　the　full　search　algorithm　with　a　negligible　quality　drop.