Computation offloading in mobile cloud computing is a critical research area that focuses on transferring computation-intensive tasks from resource-constrained mobile devices to cloud or edge servers to improve performance, reduce energy consumption, and enhance user experience. Research papers in this domain explore static, dynamic, and adaptive offloading strategies that consider device capabilities, network conditions, task requirements, latency constraints, and Quality of Service (QoS) metrics. Studies highlight the use of optimization algorithms, game-theoretic models, and machine learning techniques to make intelligent offloading decisions under varying workloads and network dynamics. Recent works also investigate collaborative and multi-tier offloading frameworks that leverage edge, fog, and cloud resources to minimize response time and maximize resource utilization. Security- and privacy-aware computation offloading mechanisms are increasingly emphasized to protect sensitive data during task migration. Additionally, energy-efficient offloading and cost-aware strategies are studied to prolong mobile device battery life while ensuring application performance. Overall, computation offloading research in mobile cloud computing enables adaptive, efficient, and secure distribution of computational workloads, bridging the gap between mobile device limitations and the high performance requirements of modern applications.