Enhancing mobile video streaming by lookahead rate allocation in wireless networks
Bibliographic record
Abstract
Developing novel video delivery mechanisms have become imperative to cope with the unprecedented growth in mobile video traffic. In this paper, we present video transmission schemes that improve the streaming experience by looking ahead at the future rates users are expected to face. Such an approach is useful for the delivery of stored videos that can be strategically buffered in advance at the users' devices. For instance, if it is known a user is entering a low coverage area, content can be prebuffered to support smooth streaming. Therefore, the Base Stations (BSs) can now plan long-term multi-user rate allocations based not only on current channel states, but also on future conditions. To provide a performance benchmark we first develop a lookahead multi-objective Linear Program (LP) that offers a trade-off between minimizing overall network video degradation, and providing fairness in individual user degradation. Then, to efficiently solve the problem, we present a polynomial-time algorithm that closely follows the pareto-optimal trade-off of the multi-objective LP. We provide an extensive performance analysis of the proposed methods by simulations, and numerical results demonstrate that significant improvements in video streaming are achievable by the lookahead rate allocation strategies.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".