Smart Caching: Empower the Video Delivery for 5G-ICN Networks
Bibliographic record
Abstract
Since multimedia services will become fundamental in the upcoming 5G networks, how to improve the user quality of experience (QoE) is becoming a major challenge. In this paper, we integrate the concept of Information-Centric Networking (ICN) to the infrastructure of 5G networks. Due to the in-network caching feature of ICN, proactive caching can be beneficial in 5G networks. More precisely, this paper introduces a novel proactive caching approach (called smart caching) which leverages the non-negative matrix factorization (NMF) technique to predict the future ratings of user preferences on all videos for 5G-ICN networks. To solve the shortcoming of the NMF technique that generates inaccurate predictions for high rated but unpopular videos, we also take video historical popularity into consideration. Thus, the user future demands can be predicted based on the user preferences (i.e. the predicted ratings) and the historical popularity of videos. Simulation results show that the proposed smart caching outperforms existing approaches in terms of hit ratio, average video retrieval delay, and user satisfaction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".