A Smart Map Sharing and Preloading Scheme for Mobile Cloud Gaming in D2D Networks
Bibliographic record
Abstract
With the high popularizing rate of smart devices, mobile gaming is an emerging arena in game industry with the purpose of providing ubiquitous game services to mobile players. Different from the traditional player who play games on their personal computers with wired local area network or WiFi access, more and more mobile players nowadays prefer to play online games on their smart devices that communicate to cloud servers via wireless cellular networks. Therefore, in the mobile cloud gaming context, the monetary cost of downloading or updating map files in games via cellular networks is a new issue that may effect players' experience. On the other hand, an unpredictable latency may be introduced by the wireless links in the cellular network. In fact, a mobile player can preload the maps that he has high probability to go. Moreover, mobile players nearby can also form a device-to-device (D2D) communication network to share their cached maps. In this paper, we consider the problem that with the limited storage space available on each player's device, how to select the maps on either neighboring devices or cloud server to preload so that the utility of the player can be maximized. We first formula an optimization problem and then present our solution. Simulation results show that our proposed map sharing and preloading scheme can significantly increase the utility received by mobile players.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".