Channel Aware Multiuser Scalable Video Streaming Over Lossy Under-Provisioned Channels: Modeling and Analysis
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we analyze the performance of media-aware multiuser video streaming strategies in capacity limited wireless channels suffering from latency problems and packet losses. Wireless video streaming applications are characterized by their bandwidth-intensity, delay-sensitivity, and loss-tolerance. Our main contributions include (i) a rate-minimized unequal erasure protection (UXP) scheme, (ii) an analytical expression for packet delay and play-out deadline of UXP protected scalable video, (iii) a loss-distortion model for hierarchical predictive video coders with picture copy concealment, (iv) an analysis of the performance and complexity of delay-aware, capacity-aware, and optimized UXP streaming scenarios, and (v) we show that the use of unequal error protection causes a rate-constrained optimization problem to be nonconvex. Performance evaluations using a 3GPP network simulator show that, for different channel capacities and packet loss rates, delay-aware nonstationary rate-allocation streaming policies deliver 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 <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">offline</i> computation which is performed prior to the start of the streaming session or in batches during transmission and therefore, do not affect the run-time performance of the streaming system.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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