With　the　fast　development　of　smart　cities　and　5G,　the　amount　of　mobile　data　is　growing　exponentially.　The　centralized　cloud　computing　mode　is　hard　to　support　the　continuous　exchanging　and　processing　of　information　generated　by　millions　of　the　Internet-of-Things　(IoT)　devices.　Therefore,　mobile-edge　computing　(MEC)　and　software-defined　networking　(SDN)　are　introduced　to　form　a　cloud-edge-terminal　collaboration　network　(CETCN)　architecture　to　jointly　utilize　the　communicational　and　computational　resources.　Although　the　CETCN　brings　many　benefits,　there　still　exist　some　challenges,　such　as　the　unclear　operation　mode,　low　utilization　of　edge　resources,　as　well　as　the　limited　energy　of　terminals.　To　address　these　problems,　a　reinforcement　learning-based　joint　communicational-and-computational　resource　allocation　mechanism　(RJCC)　is　proposed　to　optimize　overall　processing　delay　under　energy　limits.　In　RJCC,　a　Q-learning-based　online　offloading　algorithm　and　a　Lagrange-based　migration　algorithm　are　designed　to　jointly　optimize　computation　offloading　across　multisegments　and　on　edge　platform,　respectively.　The　simulation　results　show　that　the　proposed　RJCC　outperforms　the　delay-optimal,　energy-optimal,　and　edge-to-terminal　offloading　algorithm　by　42%-74%　in　long-term　average　energy　consumption　while　maintaining　relatively　low　delay.