Mobile　edge　caching　(MEC)　has　emerged　as　a　promising　and　economical　solution　to　complement　conventional　infrastructure　caching.　Nonetheless,　the　vulnerability　of　vehicle-to-vehicle　(V2V)　links　causes　connection　loss　and　limits　data　exchange.　On　the　other　hand,　the　traditional　content　popularity-based　request　model　would　lead　to　lower　cache　hit　ratios　and　increased　content　retrieval　times.　To　tackle　this　issue,　a　novel　scheme　of　MEC-assisted　public　vehicular　network　is　proposed　in　this　study,　where　the　random　linear　network　coding　(RLNC)　based　caching　strategy　is　applied　to　allow　public　vehicles　to　simultaneously　obtain　coded　blocks　from　multiple　vehicles　and　infrastructures　on　the　move.　Besides,　a　content　request　model　that　considers　content　popularity,　historical　interest,　and　social　attributes　is　explored.　A　cost　minimization　problem　is　formulated　under　the　proposed　scheme,　which　is　a　highly　non-trivial　stochastic　problem.　To　this　end,　the　data　volume　of　V2V　offloading　is　obtained　by　a　divide　and　conquer　(DC)　algorithm,　and　then　the　caching　strategy　is　derived　by　a　heuristic-based　algorithm.　Finally,　extensive　simulations　show　that　the　proposed　content　model　can　achieve　a　higher　local　offloading　ratio　and　the　proposed　scheme　has　a　much　lower　cost　compared　to　the　baselines.