StreamCache: Popularity-based caching for adaptive streaming over information-centric networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The growing demand for video streaming is straining the current Internet, and mandating a novel approach to future Internet paradigms. The advent of Information-Centric Networks (ICN) promises a novel architecture for addressing this exponential growth in data traffic, with ubiquitous caching to facilitate video delivery. In this paper, we present a novel in-network video caching policy in ICN, named StreamCache, catering to variable video contents with different sizes and bit rates. Our objective is improving the average throughput of users which consequently enhances the Quality of Experience (QoE), under the heterogeneity of users' devices and network conditions. StreamCache is a popularity-based policy, which operates distributively at routers, designed for the online processing in order to narrow the gap between the offline theoretical optimal solution and the real-world application. StreamCache operates in rounds, making caching decisions based on video request statistics and minimal cache coordination. We show that, StreamCache achieves near-optimal performance compared with the offline benchmark scheme, DASCache and outperforms current state-of-the-art protocols, such as ProbCache by presenting an elaborate evaluation carried out on ndnSIM, over NS-3.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it