Compressed　sensing　(CS)　is　a　novel　technology　to　acquire　and　reconstruct　sparse　signals　below　the　Nyquist　rate.　It　has　great　potential　in　image　and　video　acquisition　and　processing.　To　effectively　improve　the　sparsity　of　signal　being　measured　and　reconstructing　efficiency,　an　encoding　and　decoding　model　of　residual　distributed　compressive　video　sensing　based　on　double　side　information　(RDCVS-DSI)　is　proposed　in　this　paper.　Exploiting　the　characteristics　of　image　itself　in　the　frequency　domain　and　the　correlation　between　successive　frames,　the　model　regards　the　video　frame　in　low　quality　as　the　first　side　information　in　the　process　of　coding,　and　generates　the　second　side　information　for　the　non-key　frames　using　motion　estimation　and　compensation　technology　at　its　decoding　end.　Performance　analysis　and　simulation　experiments　show　that　the　RDCVS-DSI　model　can　rebuild　the　video　sequence　with　high　fidelity　in　the　consumption　of　quite　low　complexity.　About　1~5　dB　gain　in　the　average　peak　signal-to-noise　ratio　of　the　reconstructed　frames　is　observed,　and　the　speed　is　close　to　the　least　complex　DCVS,　when　compared　with　prior　works　on　compressive　video　sensing.　Copyright　©　2014　Acta　Automatica　Sinica.　All　rights　reserved.