Minimizing end-to-end delay in three-layer MEC with dual-input requirement

Most mobile edge computing (MEC) scenarios assume that all the input data of offloading tasks is transmitted from users. However, we notice that a more general situation, i.e., at least a part of input data, as common knowledge, should be provided from outside environments (e.g., the Internet). In this paper, we formulate the end-to-end delay in the High Altitude Platform Station (HAPS) and Roadside unit (RSU) assisted MEC, where the input data of these tasks are provided from both HAPS and users/nodes. Then we adopt the branch and bound method to relax this mixed-integer nonlinear programming (MINLP) problem. The Karush-Kuhn-Tucker (KKT) conditions and the interior-point method are utilized to analyze and optimize the relaxed problem. Experimental results show that our scheme can significantly reduce the end-to-end delay of data transmission and task offloading compared to other benchmark schemes.