With　the　explosion　in　the　number　of　connected　devices　and　Internet　of　Things　(IoT)　services　in　smart　city,　the　challenges　to　meet　the　demands　from　both　data　traffic　delivery　and　information　processing　are　increasingly　prominent.　Meanwhile,　the　connected　vehicle　networks　have　become　an　essential　part　in　smart　city,　bringing　massive　data　traffic　as　well　as　significant　communication,　caching,　and　computing　resources.　As　the　two　typical　services　types　in　smart　city,　delay-tolerant　and　delay-sensitive　traffic　requires　very　different　quality　of　service　(QoS)/quality　of　experience　(QoE),　and　could　be　delivered　through　the　routes　with　different　features　to　meet　their　QoS/QoE　requirements　with　the　lowest　costs.　In　this　paper,　we　propose　a　novel　vehicle　network　architecture　in　the　smart　city　scenario,　mitigating　the　network　congestion　with　the　joint　optimization　of　networking,　caching,　and　computing　resources.　Cloud　computing　at　the　data　centers　as　well　as　mobile　edge　computing　at　the　evolved　node　Bs　and　on-board　units　are　taken　as　the　paradigms　to　provide　caching　and　computing　resources.　The　programmable　control　principle　originated　from　the　software-defined　networking　paradigm　has　been　introduced　into　this　architecture　to　facilitate　the　system　optimization　and　resource　integration.　With　the　careful　modeling　of　the　services,　the　vehicle　mobility,　and　the　system　state,　a　joint　resource　management　scheme　is　proposed　and　formulated　as　a　partially　observable　Markov　decision　process　to　minimize　the　system　cost,　which　consists　of　both　network　overhead　and　execution　time　of　computing　tasks.　Extensive　simulation　results　with　different　system　parameters　reveal　that　the　proposed　scheme　could　significantly　improve　the　system　performance　compared　to　the　existing　schemes.