Dynamic edge-caching through content popularity and crowd prediction for short video services

With the explosive growth of short video traffic and the increasing demand for low-latency content delivery, efficient edge caching strategies have become critical for mobile networks. However, the highly dynamic and personalized characteristics of short video services present substantial challenges for traditional caching approaches, which often depend exclusively on static popularity metrics. This paper proposes DECC (Dynamic Edge-caching through Content Popularity and Crowd Prediction), a novel caching framework that jointly models content popularity and user access behavior to optimize caching decisions at edge nodes. DECC integrates a hybrid deep learning architecture comprising 1D Convolutional Neural Networks (Conv1D), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRU) to capture the temporal dynamics of both video requests and user activity. A fusion mechanism is introduced to generate cache priority scores based on dual-path predictions, enabling more accurate and adaptive content placement. Experimental evaluations conducted on real-world datasets demonstrate that DECC consistently surpasses baseline methods in cache hit rate, access latency reduction, and overall resource utilization efficiency. These results highlight the potential of DECC as a scalable and intelligent caching solution for next-generation short video edge services.