Characterizing User Behaviors in Mobile Personal Livecast
Bibliographic record
Abstract
Mobile personal livecast (MPL) services are emerging and have received great attention recently. In MPL, numerous and geo-distributed ordinary people broadcast their video contents to worldwide viewers. Different from conventional social networking services like Twitter and Facebook, which have a tolerance for interaction delay, the interactions (e.g., chat messages) in a personal livecast must be in real-time with low feedback latency. These unique characteristics inspire us to: (1) investigate how the relationships (e.g., social links and geo-locations) between viewers and broadcasters influence the user behaviors, which has yet to be explored in depth; and (2) explore insights to benefit the improvement of system performance. In this article, we carry out extensive measurements of a representative MPL system, with a large-scale dataset containing 11M users. In the current costly and limited cloud-based MPL system, which is faced with scalability problem, we find: (1) the long content uploading distances between broadcasters and cloud ingesting servers result in an impaired system QoS, including a high broadcast latency and a frequently buffering events; and (2) most of the broadcasters in MPL are geographically locally popular (the majority of the views come from the same region of the broadcaster), which consume vast computation and bandwidth resources of the clouds and Content Delivery Networks. Fortunately, the emergence of edge computing, which provides cloud-computing capabilities at the edge of the mobile network, naturally sheds new light on the MPL system; i.e., localized ingesting, transcoding, and delivering locally popular live content is possible. Based on these critical observations, we propose an edge-assisted MPL system that collaboratively utilizes the core-cloud and abundant edge computing resources to improve the system efficiency and scalability. In our framework, we consider a dynamic broadcaster assignment to minimize the broadcast latency while keeping the resource lease cost low. We formulate the broadcaster scheduling as a stable matching with migration problem to solve it effectively. Compared with the current pure cloud-based system, our edge-assisted delivery approach reduces the broadcast latency by about 35%.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".