Video　broadcasting　is　becoming　more　and　more　popular　in　wireless　networks.　However,　the　existing　digital　coding　and　transmission　approaches　can　hardly　accommodate　users　with　diverse　channel　conditions,　which　is　called　the　cliff　effect.　Recently,　a　novel　video　broadcasting　method　called　SoftCast　has　been　proposed.　It　achieves　graceful　degradation　with　increasing　noise　by　making　the　magnitude　of　the　transmitted　signal　proportional　to　the　pixel　value　and　using　a　novel　power　allocation　scheme.　In　this　paper,　we　propose　a　novel　video　broadcast　method　that　exploits　deep　convolutional　networks　and　group　based　sparse　representation.　It　utilizes　the　channel　condition　information　generated　from　decoder　to　optimize　the　decoding　process　and　reduce　the　various　artifacts　caused　by　source　and　channel　coding.　By　utilizing　soft　video　broadcast　transmission,　it　achieves　good　broadcast　performance　and　avoids　the　cliff　effect.　The　experimental　results　show　that　the　proposed　scheme　provides　better　performance　compared　with　the　traditional　SoftCast　with　up　to　1.5　dB　coding　gain.