The advancement in mobile technology and the introduction of cloud computing systems enable the use of educational materials on mobile devices for a location- and time-agnostic learning process. These educational materials are delivered in the form of data and compute-intensive multimedia-enabled learning objects. Given these constraints, the desired objective of mobile learning (m-learning) may not be achieved. Accordingly, a number of m-learning systems are being developed by the industry and academia to transform society into a pervasive educational institute. However, no guideline on the technical issues concerning the m-learning environment is available. In this study, we present a taxonomy of such technical issues that can impede the life cycle of multimedia-enabled m-learning applications. The taxonomy is devised based on the issues related to mobile device heterogeneity, network performance, content heterogeneity, content delivery, and user expectation. These issues are discussed, along with their causes and measures, to achieve solutions. Furthermore, we identify several trending areas through which the adaptability and acceptability of multimedia-enabled m-learning platforms can be increased. Finally, we discuss open challenges, such as low complexity encoding, data dependency, measurement and modeling, interoperability, and security as future research directions.