In　the　Industrial　Internet　of　Things　(IIoT)　scenario,　the　increase　of　surveillance　equipment　brings　challenges　to　the　transmission　of　real-time　video.　It　needs　more　efficient　approaches　to　finish　video　transmission　with　more　stability　and　accuracy.　Therefore,　we　propose　a　self-adaptive　transmission　scheme　of　videos　for　multi-capture　terminals　under　IIoT　in　this　paper.　To　fit　for　the　constant　variation　of　network　environment,　we　compress　the　videos　that　wait　for　transmitting　from　multi-capture　terminals　by　reducing　the　non-key　frames　with　Graph　Convolutional　Network　(GCN).　Moreover,　a　self-adaptive　strategy　of　transmission　is　implemented　on　the　Mobile　Edge　Computing　(MEC)　server　to　adjust　the　transmission　volume　of　processed　videos,　and　a　multi-objective　optimization　algorithm　is　utilized　to　optimize　the　strategy　of　transmission　during　the　video　transmission.　The　relative　experiments　are　conducted　to　validate　the　performance　of　the　proposed　scheme.