The　existing　video　compressed　sensing　(CS)　algorithms　for　inconsistent　sampling　ignore　the　joint　correlations　of　video　signals　in　space　and　time,　and　their　reconstruction　quality　and　speed　need　further　improvement.　To　balance　reconstruction　quality　with　computational　complexity,　we　introduce　a　structural　group　sparsity　model　for　use　in　the　initial　reconstruction　phase　and　propose　a　weight-based　group　sparse　optimization　algorithm　acting　in　joint　domains.　Then,　a　coarse-to-fine　optical　flow　estimation　model　with　successive　approximation　is　introduced　for　use　in　the　interframe　prediction　stage　to　recover　non-key　frames　through　alternating　optical　flow　estimation　and　residual　sparse　reconstruction.　Experimental　results　show　that,　compared　with　the　existing　algorithms,　the　proposed　algorithm　achieves　a　peak　signal-to-noise　ratio　gain　of　1–3　dB　and　a　multi-scale　structural　similarity　gain　of　0.01–0.03　at　a　low　time　complexity,　and　the　reconstructed　frames　not　only　have　good　edge　contours　but　also　retain　textural　details.　©　2019　Elsevier　Inc.