With　the　proliferation　of　mobile　devices　(e.g.,　vehicles　and　smartphones),　rich　media　content　services　from　massive　users　lead　to　high　network　resource　consumption　and　energy　usage.　How　to　effectively　allocate　heterogeneous　network　resources　and　achieve　green　content　delivery　is　a　major　challenge　to　be　addressed　in　urgency,　especially　when　vehicular　users　are　involved　and　the　spatiotemporal　distribution　of　the　content　requests　can　change　drastically.　In　this　paper,　we　propose　a　new　deep　reinforcement　learning　(DRL)-aided　task　offloading　and　resource　allocation　scheme,　named　TORA-DRL,　to　minimize　power　consumption　in　cloud-edge　cooperation　environments,　where　in-network　caching　and　request　aggregation　are　incorporated　to　reduce　replicated　transmissions　of　network　contents.　Based　on　the　history　of　content　requests　and　available　network　resources　in　the　system,　TORA-DRL　jointly　optimizes　the　decisions　about　task　offloading,　as　well　as　computing,　caching　and　communication　resource　allocation,　adapting　to　the　changes　in　network　states　and　user　requirements.　Simulation　results　demonstrate　that　the　proposed　TORA-DRL　solution　converges　fast　and　has　much　higher　energy　efficiency　than　the　existing　popular　cloud-edge　cooperation　schemes　under　different　network　environments.　IEEE