Resource allocation in mobile cloud computing is a critical research area that focuses on efficiently distributing computational, storage, and networking resources among mobile devices and cloud/edge servers to optimize performance, energy consumption, and Quality of Service (QoS). Research papers in this domain explore static, dynamic, and adaptive allocation strategies that consider heterogeneous device capabilities, varying workloads, network conditions, latency requirements, and energy constraints. Studies highlight the use of optimization algorithms, game-theoretic models, machine learning, and deep reinforcement learning techniques to make intelligent resource allocation decisions under dynamic mobile cloud environments. Recent works also investigate multi-tier resource management in edge–fog–cloud architectures, addressing challenges such as load balancing, task offloading, service placement, and scalability. Security- and privacy-aware resource allocation frameworks are increasingly emphasized to protect sensitive data while ensuring efficient resource utilization. Applications span smart healthcare, autonomous vehicles, industrial IoT, intelligent transportation systems, and mobile multimedia services. Overall, research in resource allocation for mobile cloud computing enables adaptive, efficient, and reliable management of distributed resources, ensuring high-performance, energy-efficient, and secure mobile cloud services.