QoE-Oriented Resource Optimization for Mobile Cloud Gaming: A Potential Game Approach
Bibliographic record
Abstract
Cloud gaming is a novel service provisioning paradigm, which hosts video games in the cloud and transmits interactive game streaming to players via the Internet. In this model, the cloud is required to consume tremendous resources for video rendering and streaming, especially when the number of concurrent players reaches a certain level. On the other hand, different players may have distinct requirements on Quality-of-Experience, such as high video quality, low delay, etc. Under this circumstance, to ensure an overall satisfaction for all players with finite cloud resources becomes a major challenge to existing cloud services. This paper employs game theory to the cloud gaming scenario and proposes a model to meet players' overall requirements with low cost. This game is proved to be a potential game with determining a devised potential function. Our experiment has shown that, with our algorithm, players can achieve a mutually satisfactory steady state, and the system will reduce the overhead up to 50% within the time complexity of O(Mlog M), where M is the number of physical servers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".