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Coded/uncoded content placement in Mobile Edge Caching (MEC) has evolved asan efficient solution to meet the significant growth of global mobile datatraffic by boosting the content diversity in the storage of caching nodes. Tomeet the dynamic nature of the historical request pattern of multimediacontents, the main focus of recent researches has been shifted to developdata-driven and real-time caching schemes. In this regard and with theassumption that users' preferences remain unchanged over a short horizon, theTop-K popular contents are identified as the output of the learning model. Mostexisting datadriven popularity prediction models, however, are not suitable forthe coded/uncoded content placement frameworks. On the one hand, incoded/uncoded content placement, in addition to classifying contents into twogroups, i.e., popular and nonpopular, the probability of content request isrequired to identify which content should be stored partially/completely, wherethis information is not provided by existing data-driven popularity predictionmodels. On the other hand, the assumption that users' preferences remainunchanged over a short horizon only works for content with a smooth requestpattern. To tackle these challenges, we develop a Multiple-model (hybrid)Transformer-based Edge Caching (MTEC) framework with higher generalizationability, suitable for various types of content with different time-varyingbehavior, that can be adapted with coded/uncoded content placement frameworks.Simulation results corroborate the effectiveness of the proposed MTEC cachingframework in comparison to its counterparts in terms of the cache-hit ratio,classification accuracy, and the transferred byte volume.

Multi-Content Time-Series Popularity Prediction with Multiple-Model Transformers in MEC Networks