Due　to　the　limited　battery　capacity　and　computing　capability　of　mobile　users,　the　resource　allocation　strategy　in　device-to-device　(D2D)-assisted　edge　computing　system　with　hybrid　energy　harvesting　is　investigated　in　this　paper.　By　employing　magnetic　induction-based　wireless　reverse　charging　technology,　mobile　user　can　supplement　extra　energy　from　nearby　users　when　the　energy　harvested　from　ambient　radio　frequency　sources　is　about　to　be　exhausted.　Moreover,　mobile　user　can　not　only　perform　local　computation,　but　also　offload　computing　tasks　to　nearby　users　for　auxiliary　computation　through　D2D　communication　links　or　mobile　edge　computing　(MEC)　server　under　base　station　(BS)　for　edge　computation.　Due　to　the　limited　computing　resources　of　MEC　server,　when　the　computing　capability　of　the　MEC　server　reaches　the　maximum　value,　an　adjacent　user　under　another　nearby　BS　can　be　considered　as　a　relay　node.　The　computing　tasks　of　the　remaining　users　under　the　previous　BS　can　be　transferred　to　the　MEC　server　with　sufficient　resources　under　another　nearby　BS　by　establishing　D2D　relay　links.　The　objective　of　the　resource　allocation　strategy　is　to　maximize　the　energy　efficiency　under　the　constraints　of　computation　delay　and　energy　harvesting.　The　resource　allocation　problem　is　formulated　as　a　mixed-integer　nonlinear　programming　problem,　which　is　not　easy　to　obtain　the　optimal　solution　at　low　computational　complexity.　A　suboptimal　solution　is　obtained　by　adopting　the　quantum-behaved　particle　swarm　optimization　(QPSO)　algorithm.　Simulation　results　show　that　the　performance　of　the　proposed　strategy　is　superior　to　other　benchmark　strategies,　and　QPSO　algorithm　can　achieve　higher　energy　efficiency　than　the　standard　particle　swarm　optimization　algorithm.