Utility function for predicting IPTV Quality of Experience based on delay in Overlay Networks
Bibliographic record
Abstract
Service Overlay Networks (SONs) provide new complex services in the Internet without requiring major changes to underlying physical networks. A SON is an overlay network made up of virtual nodes and links on top of the existing infrastructure. Whenever a client requests a specific service, such as streaming a video on his mobile device, the SON creates a path on the fly to deliver the video stream from the server to the client. This path is called a Service Specific Overlay Network (SSON) and consists of nodes that meet the Quality of Service (QoS), Quality of Experience (QoE), and technical requirements of the user. Unfortunately, the highly dynamic nature of overlay networks makes it challenging to keep video quality at the required levels. Errors and delays due mainly to congestion cause packets to be dropped or queued for a long period of time in intermediate nodes. Video quality is greatly affected by such impairments. In this paper, we propose a utility function to predict QoE of video delivered over SSONs. The proposed function is based on application-level statistical information, namely frame delay. It allows user QoE to be monitored in real-time without incurring additional overhead on the network. When degradation in the utility is detected, appropriate adaptation schemes can be used to restore the QoE to acceptable levels. We show the effectiveness and flexibility of the proposed scheme via mathematical proofs and simulation results.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 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".