Analysis of the Effect of QoS on Video Conferencing QoE
Why this work is in the frame
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Bibliographic record
Abstract
Network service providers tend to focus on the quality of service (QoS) they provide to their customers. This entails analysis of various QoS metrics (such as bandwidth, packet loss and jitter) in order to be able to improve their services. This is a single-dimensional approach to a problem that needs to be analyzed not only from a business improvement perspective but also from a customer satisfaction perspective. QoS metrics do not directly translate to customer experience, which is more qualitative than quantitative. Thus, it is necessary to correlate qualitative metrics that customers relate to with quantitative metrics that can be analyzed and improved upon by service providers. This is a non-trivial problem that needs deeper exploration. In this paper, we attempt to correlate video conferencing QoE (Quality of Experience) with network QoS. In order to do this, we developed a novel Docker image called Lime, to be able to automate the experiments and emulate the network environment. We performed 144 separate video conferences under predefined network handicaps (scenarios). We discovered that bandwidth is directly proportional to the perceived quality of the video implying that higher bandwidth is preferred. On the other hand, frequently fluctuating bandwidth quickly reduced the user-opinion, and also resulted in slower subsequent climb in opinion after a period of high fluctuation. This indicated that steady bandwidth is preferred over irregularly increasing bandwidth. Jitter and packet loss were found to contribute to negative user-opinion as well as low bandwidth. Conversely, increasing jitter and packet loss was mostly forgiven if the bandwidth stayed stable and high. Lime is shown to be a novel tool to fulfill requirements related to video conferencing experiments under pre-defined network scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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