Cross-Layer Optimization of Fast Video Delivery in Cache- and Buffer-Enabled Relaying Networks
Bibliographic record
Abstract
In this paper, we investigate the cross-layer optimization of caching and fast video delivery for enhanced video streaming quality of experience in two-hop relaying networks, where a base station supplies video data to multiple users with the help of relays. Different from conventional systems, each half-duplex relay node is equipped with a cache and a buffer to facilitate joint scheduling of video fetching and delivery. This introduces channel diversity gains and facilitates fast video delivery. In particular, we investigate two-stage caching and delivery control schemes for the minimization of the overall video delivery time. An offline caching and delivery optimization problem, which assumes full knowledge of user requests and channel state information (CSI), is formulated but turns out to be functional and nonconvex. However, we unveil a hidden quasi-convexity and convexity in the two layers of the decomposed problem and, hence, solve the offline problem optimally and efficiently. Moreover, online video delivery control exploiting statistical CSI is investigated under a stochastic dynamic programming (DP) framework. To mitigate the high computational complexity of DP, we further propose a low-complexity online video delivery algorithm, which achieves close-to-optimal performance in the high buffer capacity regime. Simulation results show that our offline and online schemes can significantly reduce the overall video delivery time due to the degrees of freedom enabled by caching and buffering. Besides, an interesting tradeoff between caching and buffering gains in exploiting the diversity of the wireless channel is revealed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".