With　the　rapid　development　of　wireless　communication　technologies,　the　emerging　multimedia　applications　make　mobile　Internet　traffic　grow　explosively　while　putting　forward　higher　service　requirements　for　the　next-generation　wireless　networks.　Therefore,　how　to　achieve　low-latency　content　transmission　by　effectively　allocating　heterogeneous　network　resources　to　improve　the　network　quality　of　service　and　end-user　quality　of　experience　is　a　key　issue　to　be　solved　urgently　in　the　current　Internet.　In　this　article,　we　propose　a　deep　reinforcement　learning　(DRL)-based　resource　allocation　scheme　to　improve　content　distribution　in　a　layered　fog　radio　access　network　(FRAN).　We　formulate　the　optimal　resource　allocation　problem　as　a　minimal　delay　model,　where　in-network　caching　is　deployed　and　the　same　content　requests　from　mobile　users　can　be　aggregated　in　the　queue　of　each　base　station.　To　cope　with　the　increasing　user　requests　and　overcome　capacity　constraints　of　the　FRAN,　moreover,　a　cloud-edge　cooperation　offloading　scheme　is　utilized　in　our　model,　where　the　integrated　allocation　of　caching,　computing,　and　communication　resources　and　joint　optimization　between　innetwork　caching　and　routing　are　considered　to　promote　resource　utilization　and　content　delivery.　In　our　solution,　a　new　DRL　policy　is　designed　to　make　cross-layer　cooperative　caching　and　routing　decisions　for　the　arriving　content　requests　according　to　request　history　information　and　available　network　resources　in　the　system.　Simulation　results　demonstrate　that　our　proposed　model　can　performs　much　better　than　the　existing　cloud-edge　cooperation　schemes　in　the　FRAN.