Device cooperation-assisted scalable video multicast with heterogeneous QoE guarantees
Bibliographic record
Abstract
This paper proposes a device cooperation-assisted solution for efficient quality-of-experience (QoE)-differentiated scalable video multicast. The proposed solution targets a group of co-located user equipments (UEs) with heterogeneous QoE requirements that are able to cooperate with each other through direct device-to-device (D2D) short-range communication to receive the same scalable multicast video stream. Content delivery to the group of UEs under consideration occurs in two phases, namely, a multicast phase where multiple video source layers are fountain encoded and mapped to hierarchical quadrature amplitude modulation (H-QAM) symbols of varying robustness levels, followed by a cooperation phase where UEs within the multicast group use their D2D connections to help their neighbors achieve their respective QoE targets. Three main features characterize the proposed UE cooperation-assisted scalable video multicast solution: (i) hierarchical modulation is used at the physical layer to provide unequal error protection for the different layers of the scalable video stream while guaranteeing some basic quality of service for most UEs in the multicast group, (ii) UE cooperation through D2D communications is used to meet heterogeneous QoE requirements, and (iii) different video source layers are fountain encoded during both multicast and cooperation phases in order to minimize the cooperation overhead required to ensure heterogeneous QoE guarantees.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".