A Geometric Approach to Server Selection for Interactive Video Streaming
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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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".