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 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 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".