On statistical multiplexing of variable-bit-rate video streams in mobile systems
Bibliographic record
Abstract
We consider the problem of broadcasting multiple variable-bit-rate (VBR) video streams from a base station to many mobile devices over a wireless network, so that: (i) perceived quality on mobile devices is maximized, (ii) bandwidth utilization is maximized, and (iii) energy consumption of mobile devices is minimized. We show that this problem is NP-Complete. We propose an approximation algorithm for the base station to statistically multiplex and transmit multiple VBR streams to achieve these objectives. We analytically analyze the performance of our algorithm and prove that it achieves optimal bandwidth utilization and near-optimal energy saving. Our algorithm frees network operators from the manual and error-prone bandwidth reservation process, which is usually used in practice for broadcasting VBR streams. We implement the proposed algorithm in a trace-driven simulator, and conduct extensive simulations. The simulation results show that our algorithm outperforms the existing algorithms in many aspects, including number of late frames, number of concurrently broadcast video streams, and energy saving of mobile devices. We also implement the proposed algorithm in a real testbed for video broadcasting as a proof of concept. The results from the testbed confirm that the proposed algorithm: (i) does not result in playout glitches, (ii) achieves high energy saving, and (iii) runs in real time.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".