Analysis of HnH Model for Live Streaming Channels with a Small Number of Viewers
Bibliographic record
Abstract
Current trends show an increasing number of Dedicated Channels having a Small number of Viewers (DCSV for short) in multi-channel live streaming systems. Usually, DCSV channels are either user generated or dedicated channels and they suffer adversely from poor channel performance, mainly, due to having a small number of participants. As a result, when a viewer explicitly requests for a block of streaming content, the probability that the requested block of data will be available among the existing viewers is less than what is required in order to offer a continuous service. We propose HnH (short for Hand in Hand), a novel scheme of cross-channel resource sharing, in order to solve the performance problem of DCSV channels due to their small number of viewers. We next develop a discrete-time stochastic model in order to analyze the performance issues of the proposed HnH scheme and provide insight into it. Numerical experiments were conducted in order to first validate the stochastic model, and then to evaluate the performance of the HnH scheme. Experiments showed that the HnH scheme allows an improvement of the quality of service for the viewers of DCSV channels.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".