Multiplayer cloud gaming system with cooperative video sharing
Bibliographic record
Abstract
Mobile cloud-based video gaming (MCVG) is an emerging trend in moving the online entertainment industry into the cloud era. In MCVG, the game engines are hosted in the cloud, and the rendered gaming videos are transmitted over wireless networks to the mobile devices. In reverse, game players' interactions on screens are sent to the cloud server over the same networks. How to compress and transmit the real-time gaming video, so that during the gaming session, the expected server transmission rate over the bandwidth-limited wireless network is minimized while satisfying the quality of experience demanded by the players, is a great technical challenge that is addressed in this paper in a multi-player gaming context. We exploit the correlations between the gaming videos for distinct players in the same gaming scene to propose a cloud gaming system with cooperative video sharing, in which the cloud game server is able to efficiently encode and transmit multiple video streams to a group of players, while those players are able to decode their video in a cooperative manner by sharing contents via a secondary network such as ad hoc wireless local area network. Experimental results show that the expected server transmission rate can be significantly reduced compared to the conventional video encoding schemes for cloud games.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".