Delay-Aware Rate Control for Multi-User Scalable Video Streaming Over Mobile Wireless Networks
Bibliographic record
Abstract
In this paper, we propose a delay and capacity constrained multi-user scalable video streaming scheme that improves the average end-to-end distortion of transmitted video streams compared to traditional streaming strategies. Wireless video streaming applications are characterized by their bandwidth-intensity, delay-sensitivity, and loss-tolerance. Our main contributions include: (i) an analytical expression for packet delay and play-out deadline of unequal erasure protection (UXP) protected scalable video, (ii) an analysis of the performance of delay-aware, capacity-aware rate allocation for optimized UXP streaming scenarios, (iii) proof that unequal error protection causes a rate-constrained optimization problem to be non-convex. Performance evaluations using a 3GPP network simulator show that, for different channel capacities and packet loss rates, delay- aware non-stationary rate-allocation delivers significant gains which range between 1.65 dB to 2 dB in average Y-PSNR of the received video streams over delay-unaware strategies. These gains come at a cost of increased off-line computation which is performed prior to the streaming session and therefore, do not affect the run-time performance of the streaming system. .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".