A recent review and a taxonomy for multimedia application in Mobile cloud computing based energy efficient transmission

The mobile devices have entered the race of deploying high tech to the users but inherit a defect of limited battery storage. With the limitation of battery capacity, energy efficiency has been a major concern for Mobile Cloud Computing (MCC). The multimedia application comprises rich media that needs higher processing and computation, and the resource and computation are constrained. Energy-efficient transmission is a big concern because it equips the mobile device with high-end hardware components, but still, are far behind to the battery capacity and computation competence. This paper intends to introduce and investigate the different approaches/algorithms and tools that best fit to save resource utilization and energy consumption rationally. The algorithms like the Genetic Algorithm, Greedy scheduling algorithm, power-aware list-based scheduling, Offloading based task scheduling, media cloud distributed scheduling algorithm, etc. are discussed in this research. The data, Energy-efficient technique, and Cloud server response that defines each of the main components is introduced in this paper that concerns the implementation of the energy-efficient transmission in MCC. We propose these components that illustrate the workflow in the technology. A process when the data is perceived by the technique to adequately deliver a response to the mobile device. An energy-efficient transmission technique for MCC is a development towards the use of mobile cloud computing in this field working to counter the utilization of energy and resources that execute locally. This concept results in reducing the amount of data transmitted with the use of sharing the data externally between the tasks that will save energy. Through this paper, readers will understand the benefit of using the energy-efficient technique in the MCC. Also, the reader will understand the classification groups, validation criteria, future gaps of the 30 literature reviews about the technique, and how they intend to energy optimization in mobile devices.