A Geometric Approach to Server Selection for Interactive Video Streaming
Why this work is in the frame
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Bibliographic record
Abstract
Many distributed interactive multimedia applications, such as live video conferencing and video sharing, require each participating client to transmit its captured video stream to other clients via relay servers. We consider connecting multiple clients through multiple relay servers and study the server selection problem from a dense pool of content delivery network edge locations and datacenters to reduce the end-to-end delays between clients. To achieve scalability in the presence of a large number of candidate servers, we formulate server selection as a geometric problem in a delay space instead of in a graph, which turns out to be an extension of the well-known Euclidean k-median problem. We propose practical approximation schemes when using only one or two servers with theoretical worst-case guarantees as well as fast heuristics when using k servers. We demonstrate the benefit of our optimized multiserver selection schemes through extensive evaluation based on real-world traces collected from the PlanetLab and Seattle platforms, containing personal mobile devices as well as real network experiments based on a prototype implementation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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