With　the　rapid　development　of　the　Internet-of-Things　(IoT)　and　the　continuous　progress　in　software　technologies,　IoT　devices　(IoTDs)　have　been　applied　to　various　scenarios　for　executing　computation-intensive　applications.　Limited　by　many　restrictions,　IoTDs　usually　cannot　fully　meet　the　demands　of　these　applications.　In　this　context,　the　mobile　edge　computing　(MEC)　paradigm　was　proposed.　In　MEC　paradigm,　IoTDs　and　edge　servers　(ESs)　form　an　edge-enabled　radio　access　network　(E-RAN)　so　that　the　IoTDs　can　solve　the　problem　of　their　poor　computational　resources　by　transmitting　data　or　tasks　to　the　ES.　In　order　to　improve　the　Quality-of-Service　(QoS)　of　E-RAN,　network　slicing　technology　is　widely　used.　Although　the　combination　of　MEC　and　network　slicing　technology　has　effectively　made　up　for　the　deficiencies　of　IoTDs,　the　resource　efficiency　of　network　slicing　is　still　a　serious　challenge.　This　paper　considers　a　multi-IoTD　and　multi-ES　network　system,　where　there　are　multiple　service　providers　(SPs)　with　different　priorities.　In　this　system,　the　joint　optimization　of　IoTDs　access　and　bandwidth　resource　allocation　is　formulated,　whose　objective　is　to　maximize　the　system　th.　To　address　this　problem,　we　develop　an　optimization　algorithm　including　greedy-based　devices　access　and　bandwidth　resource　allocation　optimization　(GDABRA)　algorithm.　Extensive　simulation　results　are　provided　to　demonstrate　the　system　throughput　growth　of　the　proposed　algorithm　in　comparison　with　benchmark　algorithms.