Recently,　mobile　applications　such　as　augmented　reality,　virtual　reality　and　face　recognition　have　become　more　and　more　ubiquitous.　These　delay-sensitive　applications　demand　intensive　computation　and　high　energy　consumption.　However,　mobile　devices　have　limited　battery　power　and　computation　resources　that　influence　the　quality　of　experience　(QoE)　of　mobile　users.　Mobile　edge　computing　(MEC)　has　become　a　key　technology　to　meet　these　demands.　The　crucial　challenges　regarding　MEC　paradigm　are　computation　offloading　decision,　spectrum　and　computation　resource　allocation　for　offloading　users.　In　order　to　deal　with　these　challenges,　this　paper　formulates　a　joint　delay-energy　optimization　problem　by　jointly　considering　spectrum　resource　Allocation,　computation　resource　Allocation　and　computation　Offloading　decision　(AAO).　Further,　we　transform　the　original　problem　into　convex　optimization　and　solve　the　optimization　problem　through　an　alternating　direction　method　of　multipliers　(ADMM)　algorithm　in　a　distributed　way.　Finally,　simulation　results　validate　the　efficiency　of　the　proposed　AAO　in　saving　delay　and　energy　consumption.