Stochastic Rate Control for Scalable VBR Video Streaming over Wireless Networks
Why this work is in the frame
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Bibliographic record
Abstract
Video streaming over wireless links is a challenging problem due to both the unreliable, time-varying nature of the wireless channel and the stringent delivery requirements of media traffic. Layered encoded video can be used to improve the system performance by adapting the sending rate for different video frame layers to the varying network and playout situations. In this paper, we study the adaptive control of sending rates for both the base layer and enhancement layer based on feedback information from the wireless receiver. We formulate the problem in a framework of Markov decision processes to minimize a weighted sum of video quality and playout continuity degradation. In order to decrease the computation complexity, we then develop an online greedy algorithm, which only considers the current control time period. Simulation results show that the propose adaptive rate control provides significantly improved video quality and playout smoothness. Furthermore, when rate control is not performed very frequently, the greedy algorithm achieves a video distortion rate nearly matching that of the ideal optimal dynamic programming policy.
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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 | 0.000 |
| 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