Leveraging Graph Theory for Efficient Cache Policy Design in $360^{\circ}$ Video Streaming
Bibliographic record
Abstract
Immersive systems and$360^{\circ}$video have grown in popularity in recent years. Nevertheless, due to the high bandwidth needs and massive size of these videos, streaming them presents an important issue. A possible solution to this problem is to employ edge caching, which keeps a portion of the video closer to the viewer to reduce latency and assure a higher Quality of Experience (QoE). In this paper, we present a caching policy approach for$360^{\circ}$video streaming that employs a tile-based graph representation of videos. Our method focuses on finding the most relevant tiles for caching. The suggested solution is intended to work efficiently for many videos competing for a single cache, ensuring that the most relevant tiles are cached to maximize performance. It exhibits robustness even in scenarios with a limited number of users, while still being able to effectively handle a vast amount of videos. Our technique can adapt to different video content and user needs by using the graph structure of the videos, making it a flexible and scalable solution for cache management in video streaming. Performance evaluation demonstrate the effectiveness of our method that outperforms existing caching strategies in terms of Cache Hit Ratio (CHR).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".