Utilization of Stochastic Modeling for Green Predictive Video Delivery Under Network Uncertainties
Bibliographic record
Abstract
Predictive resource allocation (PRA) has gained momentum in the network research community as a way to cope with the exponential increase in video traffic. Existing PRA schemes have demonstrated profound energy savings and ubiquitous quality of service (QoS) satisfaction under idealistic prediction of future network states. In this paper, we relax the main assumption of existing PRA work and tackle uncertainties in predicted information which resulted from space and time variation of the network load and users demands. A robust green PRA (R-GPRA) is proposed to: model the uncertainties as random variables, ensure a probabilistic satisfaction of QoS constraints, and follow a risk-aware preallocation of future demand. A recourse programming model is used to represent the tradeoff between the energy-savings and the risk of wasting resources while considering the probability of a user terminating the video session at each time slot. Thus, the scheme prevents the network from prebuffering the future video content that might be skipped by the user. Similarly, a chance constrained programming model is proposed to provide a probabilistic QoS representation to guarantee that the sum of resources, predetermined to video streaming users, do not surpass the total time-varying network capacity. We prove that a near-optimal solution is attainable by proposing a guided heuristic search with small optimality gap to numerical methods. Simulation results demonstrate the ability of R-GPRA to deliver energy-efficient video streaming with less resources than existing PRA while promising QoS satisfaction. These results provide the incentive to implement the R-GPRA in future wireless networks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".