A Hybrid Rate Adaptation Framework for MPEG-4 FGS Video Streaming Over IP
Bibliographic record
Abstract
There are increasing demands for real-time streaming video applications over the Internet. However, the current generation Internet was not originally designed for real-time streaming applications and only provides best-effort services, so there are many challenges in the deployment of video streaming applications over the Internet. This thesis investigates a hybrid end-to-end rate adaptation framework that provides application-level enhancements to achieve Quality of Service (QoS) for MPEG-4 FGS-Encoded video bandwidth on the path and the terminal process capabilities based on the packet-loss ratio and then determine their subscribing rate of video streams. The sender adjusts the transmission rate based on the packet-loss ratio and then determine their subscribing rate of video streams. The sender adjusts the transmission rate based on the proportion of load status feedbacks from the receivers. The sender and the receivers act together to minimize the possibility of network congestion by adjusting the transmission rate to match the network conditions. This framework achieves inter-receiver fairness in a heterogeneous multicast environment and improves QoS stability for MPEG-4 FGS video streaming over the Internet.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".