Managing QoS requirements for video streaming: from intra‐node to inter‐node
Bibliographic record
Abstract
Abstract Streaming video over IP networks has become increasingly popular; however, compared to traditional data traffic, video streaming places different demands on quality of service (QoS) in a network, particularly in terms of delay, delay variation, and data loss. In response to the QoS demands of video applications, network techniques have been proposed to provide QoS within a network. Unfortunately, while efficient from a network perspective, most existing solutions have not provided end‐to‐end QoS that is satisfactory to users. In this paper, packet scheduling and end‐to‐end QoS distribution schemes are proposed to address this issue. The design and implementation of the two schemes are based on the active networking paradigm. In active networks, routers can perform user‐driven computation when forwarding packets, rather than just simple storing and forwarding packets, as in traditional networks. Both schemes thus take advantage of the capability of active networks enabling routers to adapt to the content of transmitted data and the QoS requirements of video users. In other words, packet scheduling at routers considers the correlation between video characteristics, available local resources and the resulting visual quality. The proposed QoS distribution scheme performs inter‐node adaptation, dynamically adjusting local loss constraints in response to network conditions in order to satisfy the end‐to‐end loss requirements. An active network‐based simulation shows that using QoS distribution and packet scheduling together increases the probability of meeting end‐to‐end QoS requirements of networked video. Copyright © 2005 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".