Channel Adaptive Multi-User Scalable Video Streaming with Unequal Erasure Protection
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we propose a solution to the problem of streaming multiple video sequences in a capacity constrained, error-prone network. This scenario arises in situations such as multiple mobile computers accessing streamed video through a single access point, or multiple cell phone users serviced by a single base-station. Our approach constitutes a rate-constrained distortion-minimized streaming scheme to allocate the appropriate rate for each video stream that achieves minimal distortion without violating the capacity constraint. Moreover, we developed an unequal erasure protection (UXP) scheme that minimizes the protected bitstream rate and the expected packet loss probability for each video frame. Performance evaluations show that this approach delivers an improved decoded video quality and fairer rate-allocation when compared with standard UXP and rate- allocation schemes, specifically in limited capacity and congested channels.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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