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Dynamic edge-caching through content popularity and crowd prediction for short video services.
Sen Niu1, Yuhe Liu1, Kaili Liao2
1School of Computer and Information Engineering, Institute for Artificial Intelligence, Shanghai Polytechnic University, Shanghai, 201209, China.
Scientific Reports
|November 26, 2025
Summary
Dynamic Edge-caching through Content Popularity and Crowd Prediction (DECC) improves mobile network caching for short videos. This AI framework enhances cache hit rates and reduces latency by predicting content popularity and user behavior.
Area of Science:
- Computer Science
- Network Engineering
- Artificial Intelligence
Background:
- Short video traffic growth necessitates efficient mobile network caching.
- Traditional caching methods struggle with dynamic, personalized short video content due to reliance on static popularity metrics.
Purpose of the Study:
- To propose DECC (Dynamic Edge-caching through Content Popularity and Crowd Prediction), a novel framework for optimizing edge caching in mobile networks.
- To address the limitations of traditional caching by jointly modeling content popularity and user access behavior.
Main Methods:
- DECC utilizes a hybrid deep learning architecture (Conv1D, LSTM, GRU) to analyze temporal dynamics of video requests and user activity.
- A fusion mechanism generates cache priority scores via dual-path predictions for adaptive content placement.
Main Results:
- DECC significantly improves cache hit rate compared to baseline methods.
- The framework effectively reduces access latency and enhances overall resource utilization efficiency.
- Experimental evaluations on real-world datasets validate DECC's performance.
Conclusions:
- DECC offers a scalable and intelligent solution for edge caching in next-generation short video services.
- The joint modeling of content popularity and crowd prediction optimizes caching decisions for dynamic content.
- DECC demonstrates superior performance in key metrics for mobile network efficiency.
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