Mobile Video Streaming over Dynamic Single-Frequency Networks
Bibliographic record
Abstract
The demand for multimedia streaming over mobile networks has been steadily increasing over the past several years. For instance, it has become common for mobile users to stream full TV episodes, sports events, and movies while on the go. Unfortunately, this growth in demand has strained the wireless networks despite the significant increase of their capacities with recent generations. Hence, efficient utilization of the expensive and limited wireless spectrum remains an important problem, especially in the context of multimedia streaming services that consume a large portion of the bandwidth capacity. In this article, we introduce the idea of dynamically configuring cells in wireless cellular networks to form single-frequency networks based on the multimedia traffic demands from users in each cell. We formulate the resource allocation problem in such complex networks with the goal of maximizing the number of served multimedia streams, and we prove that this problem is NP-Complete. Then we present an optimal solution to maximize the number of served multimedia streams within a cellular network. This optimal solution, however, may suffer from an exponential time complexity in the worst case, which is not practical for real-time streaming over large-scale networks. Therefore, we propose a heuristic algorithm with polynomial running time to provide faster and more practical solution for real-time deployments. Through detailed packet-level simulations, we assess the performance of the proposed algorithms with respect to the average service ratio, energy saving, video quality, frame loss rate, initial buffering time, rate of re-buffering events, and bandwidth overhead. We show that the proposed algorithms achieve substantial improvements in all of these performance metrics compared to the state-of-the-art approaches. For example, for the service ratio metric, our algorithms can serve up to 11 times more users compared to the unicast approach, and they achieve up to 54% improvement over the closest multicast approaches in the literature.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| 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".