Energy-Aware and Bandwidth-Efficient Hybrid Video Streaming Over Mobile Networks
Why this work is in the frame
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Bibliographic record
Abstract
Current cellular networks support video streaming over unicast or multicast. However, there exists a tradeoff between utilizing the two: i) unicast leads to higher network load, but lower energy consumption of mobile devices, and ii) multicast results in lower network load, but higher energy consumption. To make the best out of both, we propose to concurrently utilize unicast and multicast for minimizing the energy consumption of mobile devices and minimizing the load on cellular networks. Cellular networks support two multicast schemes: i) independent cell networks and ii) multi-cell single frequency networks, where multiple adjacent base stations operate on the same frequency. We first consider the less-complicated independent cell networks, and then extend our solution to single frequency networks for better performance. We formulate the resource allocation in hybrid multicast -unicast streaming systems as a binary integer programming problem. We describe optimal algorithms for the two multicast schemes. We then propose two efficient, heuristic, algorithms that run faster and provide close to optimal results. While our solution is general, for concreteness, we conduct detailed LTE packet-level simulations using OPNET. Our simulation results show the proposed algorithms i) scale to many more mobile devices than the state-of-the-art unicast-only approaches and ii) result in lower energy consumption than the latest multicast-only approaches. In addition, the algorithms designed for multi-cell single frequency networks outperform the algorithms designed for independent cell networks in all aspects, such as service ratio, spectral efficiency, energy saving, video quality, frame loss rate, initial buffering time, and number of re-buffering events.
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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 | 0.000 | 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 it