Efficient packet scheduling for heterogeneous multimedia provisioning over broadband satellite networks: An adaptive multidimensional QoS‐based design
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Bibliographic record
Abstract
Abstract With their inherent broadcast capabilities and reliable extensive geographical coverage, the broadband satellite networks are emerging as a promising approach for the delivery of multimedia services in 3G and beyond systems. Given the limited capacity of the satellite component, to meet the diverse quality of service (QoS) demands of multimedia applications, it is highly desired that the available resources can be adaptively utilized in an optimized way. In this paper, we draw our attention on the development and evaluation of an efficient packet scheduling scheme in a representative broadband satellite system, namely satellite digital multimedia broadcasting (SDMB), which is positioned as one of the most attractive solutions in the convergence of a closer integration with the terrestrial mobile networks for a cost‐effective delivery of point‐to‐multipoint services. By taking into account essential aspects of a successful QoS provisioning while preserving the system power/resource constraints, the proposed adaptive multidimensional QoS‐based (AMQ) packet scheduling scheme in this paper aims to effectively satisfy diverse QoS requirements and adaptively optimize the resource utilization for the satellite multimedia broadcasting. The proposed scheme is formulated via an adaptive service prioritization algorithm and an adaptive resource allocation algorithm. By taking into account essential performance criteria, the former is capable of prioritizing contending flows based on the QoS preferences and performance dynamics, while the latter allocates the resources, in an adaptive manner, according to the current QoS satisfaction degree of each session. Simulation results show that the AMQ scheme achieves significantly better performance than those of existing schemes on multiple performance metrics, e.g. delay, throughput, channel utilization and fairness. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.001 | 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