Multiview　video　involves　a　huge　amount　of　data,　and　as　such,　efficiently　encoding　each　view　is　a　critical　issue　for　its　wider　application.　In　this　paper,　a　fast　motion　and　disparity　estimation　algorithm　is　proposed,　utilizing　the　close　correlation　between　temporal　and　interview　reference　frames.　First,　a　reliable　predictor　is　found　according　to　the　correlation　of　motion　and　disparity　vectors.　Second,　an　iterative　search　process　is　carried　out　to　find　the　optimal　motion　and　disparity　vectors.　The　proposed　algorithm　makes　use　of　the　prediction　vector　obtained　in　the　previous　motion　estimation　for　the　next　disparity　estimation　and　achieves　both　optimal　motion　and　disparity　vectors　jointly.　Experimental　results　demonstrate　that　the　proposed　algorithm　can　successfully　save　an　average　of　86%　of　computational　time　with　a　negligible　quality　drop　when　compared　to　the　joint　multiview　video　model　(JMVM)　full　search　algorithm.　Furthermore,　in　comparison　with　the　conventional　simulcast　coding,　the　proposed　algorithm　enhances　the　video　quality　and　also　greatly　increases　coding　speed.