The　combination　of　digital　twin　(DT)　and　Internet　of　Vehicles　(IoV)　has　gained　significant　attention　from　both　academia　and　industry　in　recent　times.　DT　can　establish　a　high　fidelity　virtual　representation　of　IoV　based　on　the　real-time　sensor　data,　and　feedback　the　decision　policy,　therefore　generating　possible　improvements.　Especially,　as　the　advance　of　Mobile　Edge　Computing　(MEC)　technique,　it　has　the　potential　to　facilitate　digital　twins　computationally　intensive　tasks.　However,　how　to　schedule　the　MEC　computing　resource　is　the　key　to　efficient　operation　of　the　whole　system.　Therefore,　this　study　aims　to　investigate　pricing　considerations　and　resource　management　that　exist　between　the　vehicle　and　MEC　server　in　order　to　mitigate　this　issue.　Specifically,　we　model　the　interaction　between　the　MEC　server　and　vehicles　as　a　Stackelberg　game,　where　the　leader　(i.e.,　the　MEC　service　provider)　sets　prices,　and　then　the　vehicles　act　as　followers.　By　leveraging　information　about　social　interactions　from　other　vehicles,　utility　functions　are　formulated　by　the　vehicles.　Additionally,　the　study　analyzes　the　existence　and　uniqueness　of　the　Stackelberg　equilibrium,　and　proposes　a　dynamic　iterative　algorithm　to　find　the　appropriate　Nash　equilibrium　for　the　proposed　Stackelberg　game.　Experimental　results　demonstrate　that　the　proposed　scheme　effectively　formulates　suitable　prices　and　meets　computational　requirements.