QoE evaluation of multimedia transmission over wireless networks
Bibliographic record
Abstract
Many simulation tools (e.g., NS-2) can examine the quality of service (QoS) of networks but cannot demonstrate the visual and auditory effects of wireless transmission on multimedia quality. This paper presents a wireless multimedia simulator (WMS), which uses a compact graphical user interface to present the real-time packet delay with the playback of streaming media over a wireless channel based on classic radio channel models and IEEE 802.11 medium access control. By using captured packets and reproduced traces, WMS can demonstrate the visual and auditory effects of fading errors, packet delay and loss. Leveraging the real-time playback function, WMS enables quality of experience (QoE) evaluation of multimedia transmission in a controlled wireless environment. We carried out QoE evaluation with 30 participants for 104 test cases comprising 2 videos and 2 audio clips produced by WMS. The valuable test results enable us to quantify the relationship of QoE in terms of mean opinion score (MOS) with network traffic load and QoS metrics such as bit error probability (BEP). We find the subjective QoE is sensitive to media content although consistent with objective QoS metrics. Statistical difference-of-means tests show the video with slower motion and fewer colours is likely to offer better delay tolerance, and audio is less sensitive to bit errors while video is more resistant to network congestion.
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How this classification was reachedexpand
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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".