Towards Smart Routing: Exploiting User Context for Video Delivery in Mobile Networks
Bibliographic record
Abstract
Mobile networks are undergoing active enhancement and fast evolution, so as to host the ever-growing data traffic, mainly fuelled by video services. Despite ongoing efforts to improve the last-hop transmission in Radio Access Networks (RANs), traffic scheduling and routing in core networks remain challenging. In a system swamped with video requests, the core network needs first to schedule the transmission rate for each request, then to redirect requests to respective source nodes, and finally to route so-determined peer-to-peer flows. Towards smart routing, this paper focuses on the following two problems: (1)how to manage Quality of Experience (QoE) of video streaming services, and (2) how to optimize request routing in the core network. We exploit user context and formulate a joint problem simultaneously addressing these problems. We analyze the hardness of the formulated problem and propose a fast approximate routing algorithm, which adaptively schedules transmission rate and strategically routes the scheduled video demands. Theoretical analysis and computer simulations are then carried out to study the efficiency of the proposed algorithm.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".