  Mobile edge computing (MEC) enables resource-limited IoT devices to complete
computation-intensive or delay-sensitive task by offloading the task to
adjacent edge server deployed at the base station (BS), thus becoming an
important technology in 5G and beyond. Due to channel occlusion, some users may
not be able to access the computation capability directly from the BS.
Confronted with this issue, many other devices in the MEC system can serve as
cooperative nodes to collect the tasks of these users and further forward them
to the BS. In this paper, we study a MEC system in which multiple users
continuously generate the tasks and offload the tasks to the BS through a
cooperative node. As the tasks are continuously generated, users should
simultaneously execute the task generation in the current time frame and the
task offloading of the last time frame, i.e. the task is processed in a
streaming model. To optimize the power consumption of the users and the
cooperative node for finishing these streaming tasks, we investigate the
duration of each step in finishing the tasks together with multiuser offloading
ratio and bandwidth allocation within two cases: the BS has abundant
computation capacity (Case I) and the BS has limited computation capacity (Case
II). For both cases, the formulated optimization problems are nonconvex due to
fractional structure of the objective function and complicated variable
coupling. To address this issue, we propose optimal solution algorithm with low
complexity. Finally, simulation is carried out to verify the effectiveness of
the proposed methods and reveal the performance of the considered system.
