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Mobile edge computing (MEC) enables resource-limited IoT devices to completecomputation-intensive or delay-sensitive task by offloading the task toadjacent edge server deployed at the base station (BS), thus becoming animportant technology in 5G and beyond. Due to channel occlusion, some users maynot be able to access the computation capability directly from the BS.Confronted with this issue, many other devices in the MEC system can serve ascooperative nodes to collect the tasks of these users and further forward themto the BS. In this paper, we study a MEC system in which multiple userscontinuously generate the tasks and offload the tasks to the BS through acooperative node. As the tasks are continuously generated, users shouldsimultaneously execute the task generation in the current time frame and thetask offloading of the last time frame, i.e. the task is processed in astreaming model. To optimize the power consumption of the users and thecooperative node for finishing these streaming tasks, we investigate theduration of each step in finishing the tasks together with multiuser offloadingratio and bandwidth allocation within two cases: the BS has abundantcomputation capacity (Case I) and the BS has limited computation capacity (CaseII). For both cases, the formulated optimization problems are nonconvex due tofractional structure of the objective function and complicated variablecoupling. To address this issue, we propose optimal solution algorithm with lowcomplexity. Finally, simulation is carried out to verify the effectiveness ofthe proposed methods and reveal the performance of the considered system.

Joint Task Offloading and Resource Allocation for Streaming Application in Cooperative Mobile Edge Computing