Aiming　at　the　problem　that　the　ground　terminals　(GTs)　often　suffer　from　difficulties　in　energy　harvesting　and　data　processing　in　remote　areas,　a　resource　allocation　strategy　for　layered　unmanned　aerial　vehicles　(UAVs)-assisted　mobile　edge　computing　system　is　investigated　in　this　paper.　According　to　different　functions,　the　UAVs　are　deployed　in　three　layers.　By　using　a　magnetic　coupling　resonance　wireless　power　transfer　(MCR-WPT)　technology,　the　GTs　can　obtain　sufficient　energy　from　the　first　layer　of　UAVs　equipped　with　transmitting　coils.　The　computational　tasks　of　the　GTs　are　divided　into　popular,　private,　and　non-popular　tasks.　The　popular　and　private　tasks　are　both　offloaded　to　the　second　layer　of　popular　tasks　UAVs　(PT-UAVs),　while　the　non-popular　tasks　are　offloaded　to　the　third　layer　of　non-popular　tasks　UAV　(NPT-UAV).　The　resource　allocation　problem　is　formulated　as　an　optimization　problem.　The　optimization　objective　is　to　minimize　the　system　overhead　by　jointly　optimizing　the　PT-UAVs　caching　policy,　the　GTs　partial　offloading　factor,　the　charging　time　of　the　GTs,　the　trajectory　of　the　NPT-UAV,　and　the　bandwidth　and　computational　resource　of　the　system.　The　suboptimal　solution　is　derived　by　introducing　a　social　learning　particle　swarm　optimization　(SLPSO)　algorithm.　Simulation　results　show　that　the　SLPSO　algorithm　outperforms　other　benchmark　methods　in　terms　of　the　system　overhead.　©　2023　IEEE.