Statistical multiplexing of variable-bit-rate videos streamed to mobile devices
Bibliographic record
Abstract
We address the problem of broadcasting multiple video streams over a broadcast network to many mobile devices, so that: (i) streaming quality of mobile devices is maximized, (ii) energy consumption of mobile devices is minimized, and (iii) goodput in the network is maximized. We consider two types of broadcast networks: closed-loop networks, in which all video streams are jointly encoded to ensure their total bit rate does not exceed the broadcast network bandwidth, and open-loop networks, in which videos are encoded using standalone coders, and thus must be carefully broadcast to avoid playout glitches. We first show that the problem of optimally broadcasting multiple videos is NP-complete. We then propose an approximation algorithm to construct burst schedules for multiple VBR (Variable-Bit-Rate) streams. The proposed algorithm frees network operators from the manual and error-prone bandwidth reservation process which is currently used in practice. We prove that the proposed algorithm achieves optimal goodput and near-optimal energy saving. We show that it produces glitch-free schedules in closed-loop networks, and it minimizes number of glitches in open-loop networks. We implement the proposed algorithm in a trace-driven simulator, and conduct extensive simulations for both open- and closed-loop networks. The simulation results show that the proposed 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. To show the practicality and efficiency of the proposed algorithm, we also implement it in a real mobile TV testbed 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".