A revenue-rewarding scheme of providing incentive for cooperative proxy caching for media streaming systems
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
Network entities cooperating together can improve system performance of media streaming. In this paper, we address the “ incentive issue ” of a cooperative proxy caching system and how to motivate each proxy to provide cache space to the system. To encourage proxies to participate, we propose a “ revenue-rewarding scheme ” to credit the cooperative proxies according to the resources they contribute. A game-theoretic model is used to analyze the interactions among proxies under the revenue-rewarding scheme. We propose two cooperative game settings that lead to optimal situations. In particular, (1) We propose a distributed incentive framework for peers to participate in resource contribution for media streaming; (2) Proxies are encouraged to cooperate under the revenue-rewarding scheme; (3) Profit and social welfare are maximized in these cooperative games; and (4) Cost-effective resource allocation is achieved in these cooperative games. Large scale simulation is carried out to validate and verify the merits of our proposed incentive schemes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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