This paper studies the problem of efficient multi-user computation offloading in mobile-edge cloud computing (MEC) in a multi-channel wireless interference environment. The authors show that finding a centralized optimal solution is NP-hard and propose a game-theoretic approach to achieve efficient computation offloading in a distributed manner. They formulate the problem as a multi-user computation offloading game and analyze its structural properties, showing that it admits a Nash equilibrium and possesses the finite improvement property. A distributed computation offloading algorithm is then designed to achieve the Nash equilibrium, with an upper bound on convergence time and efficiency ratio compared to centralized solutions. The algorithm is extended to a multi-channel wireless contention environment, and numerical results demonstrate its superior performance and scalability with increasing user size. The key challenges addressed are: (1) deciding whether to compute locally or offload to the cloud, and (2) choosing an appropriate channel for efficient wireless access. The game-theoretic approach enables distributed decision-making, reducing the burden on the cloud operator and allowing users to act in their own interests while ensuring no unilateral deviation from the Nash equilibrium. The algorithm is shown to converge to a Nash equilibrium within a finite number of decision slots, with computational complexity depending on the number of channels and users. The paper also analyzes the performance of the algorithm in terms of the number of beneficial cloud computing users and system-wide computation overhead, showing that it achieves near-optimal results.This paper studies the problem of efficient multi-user computation offloading in mobile-edge cloud computing (MEC) in a multi-channel wireless interference environment. The authors show that finding a centralized optimal solution is NP-hard and propose a game-theoretic approach to achieve efficient computation offloading in a distributed manner. They formulate the problem as a multi-user computation offloading game and analyze its structural properties, showing that it admits a Nash equilibrium and possesses the finite improvement property. A distributed computation offloading algorithm is then designed to achieve the Nash equilibrium, with an upper bound on convergence time and efficiency ratio compared to centralized solutions. The algorithm is extended to a multi-channel wireless contention environment, and numerical results demonstrate its superior performance and scalability with increasing user size. The key challenges addressed are: (1) deciding whether to compute locally or offload to the cloud, and (2) choosing an appropriate channel for efficient wireless access. The game-theoretic approach enables distributed decision-making, reducing the burden on the cloud operator and allowing users to act in their own interests while ensuring no unilateral deviation from the Nash equilibrium. The algorithm is shown to converge to a Nash equilibrium within a finite number of decision slots, with computational complexity depending on the number of channels and users. The paper also analyzes the performance of the algorithm in terms of the number of beneficial cloud computing users and system-wide computation overhead, showing that it achieves near-optimal results.