NewsCast: an adaptive video stream production and delivery system
Bibliographic record
Abstract
Mobile consumers increasingly expect that their applications be context-aware by sensing their dynamic environment to deliver personalized contents. Context sources include user profiles, web history, and the environment captured by mobile device sensors. The benefits of context-aware applications and services include improved user experiences, higher quality of service, optimized system resource utilization, and smarter recommendations. One application domain that has yet to exploit personal context fully is video streaming. Video services, such as Youtube and Netflix, stream the same video clip to millions of users. However, MOOC (Massive Open Online Courses) providers, such as Udacity, Coursera or edX, offer video interaction capabilities to personalize the viewing experience. In this paper, we present the design and evaluation of NewsCast, an application to provide personalized interactive video streaming in real-time. We show how to design, adapt, and customize the NewsCast streaming experience using the Gstreamer open source software. To evaluate the performance of our context-aware video streaming platform, we subjected NewsCast to several deployment scenarios to compare key indicators such as latency, bandwidth and processor utilization. This NewsCast project grew out of the NSERC Strategic Network for Smart Applications on Virtual Infrastructure (SAVI) and our quest to build canonical, data-intensive future Internet applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".