E2E Performance Modeling for Slice-Based Video Streaming With Layered Encoding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we present a performance analytical model for end-to-end (E2E) service provisioning (i.e., processing or transmission) of layer-encoded video packets over a network slice in the core network. The disparate service reliability requirements of base layer (BL) and enhancement layer (EL) packets are considered in the proposed analytical model for the E2E packet delays, deadline violation probabilities, and throughputs of BL and EL packets. Specifically, a network function virtualization (NFV) node along the routing path of the video streaming slice is split into two consecutive logical nodes, one for packet processing and the other for transmission, based on which a segment-based analysis framework is proposed for E2E service performance modeling. A two-stage queuing model is established to obtain the approximate steady-state probability distribution of queue length at the first node in the first segment, upon which the BL/EL packet delay, deadline violation probability, and throughput at the segment are derived. In addition, the inter-departure time of successive packets departing from the first segment is analyzed based on an approximate M/D/1 system, and the packet departure process at the first segment is approximated as a Poisson process under the assumption of a large packet service rate of the first node. The independence between two consecutive segments is then achieved for analysis tractability, based on which the E2E performance measures are derived. Extensive simulation results demonstrate the accuracy of our proposed performance analytical model and its effectiveness such as in transport parameter determination.
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 | 0.000 | 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 | 0.001 | 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