A　tremendous　amount　of　visual　data　are　bing　collected　by　the　Internet　of　Video　Things　(IoVT)　systems　in　which　ubiquitous　cameras　deployed　in　cities　enable　new　applications　in　the　domains　of　smart　transportation　and　public　security.　However,　the　limited　resources　in　terms　of　communication,　computing,　and　caching　(3C)　in　the　conventional　cellular　network　make　it　challenging　to　adopt　centralized　artificial　intelligence　(AI)　to　conduct　real-time　video-based　data　analytics.　In　this　work,　based　on　the　5G　network　architecture　with　edge　servers,　a　three-phase　resource-effective　solution　is　proposed　to　perform　surveillance　operations　in　a　large-scale　wireless　IoVT.　The　proposed　strategy　integrates　front-end　cameras　with　simple　on-chip　neural　networks　performing　real-time　object-of-interest　segmentation,　edge　servers,　and　cloud　servers　with　AI　functionality　carrying　out　image-based　target　recognition　and　video-based　target　analytics　tasks.　More　importantly,　we　design　the　optimal　3C　strategy　to　achieve　the　best　video　analytics　performance　constrained　by　computing　offload　ratio,　network　resource　allocation　and　video-related　parameters.　Extensive　simulations　with　deep　neural　networks　implemented　both　at　the　front-end　cameras　and　in　the　cloud　server　have　validated　the　effectiveness　of　the　proposed　solution.