EDASH: Energy-Aware QoE Optimization for Adaptive Video Delivery over LTE Networks
Bibliographic record
Abstract
Dynamic adaptive streaming over HTTP (DASH) has emerged as a popular Internet video service, constituting a growing fraction of LTE network traffic today. We identify the root causes of DASH performance problems in bit-ate stability, energy consumption of User Equipment (UE) and efficiency of bandwidth utilization, from both the users' and network operators' perspectives. Unlike the existing researches that separately studied two important performance metrics in DASH, i.e., Quality of Experience (QoE) and UEs' energy consumption. We propose an energy-aware DASH delivery framework over LTE networks (EDASH), jointly optimizing the network throughput, users' QoE and UEs' energy efficiency. We formulate the bandwidth allocation problem as a nonlinear integer program, and design the EDASH Online Allocation algorithm (EOA). EOA assigns bandwidth based on channel conditions and buffer occupancy of UEs to achieve efficient video delivery among multiple users. Furthermore, we present the detailed design and implementation of EDASH using Apache HTTP server and Android smartphones. Both simulation and experiment results reveal that our scheme can improve the network throughput while striking a better balance between users' QoE and UEs' energy consumption.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".