  As live streaming services skyrocket, Crowdsourced Cloud-edge service
Platforms (CCPs) have surfaced as pivotal intermediaries catering to the
mounting demand. Despite the role of stream scheduling to CCPs' Quality of
Service (QoS) and throughput, conventional optimization strategies struggle to
enhancing CCPs' revenue, primarily due to the intricate relationship between
resource utilization and revenue. Additionally, the substantial scale of CCPs
magnifies the difficulties of time-intensive scheduling. To tackle these
challenges, we propose Seer, a proactive revenue-aware scheduling system for
live streaming services in CCPs. The design of Seer is motivated by meticulous
measurements of real-world CCPs environments, which allows us to achieve
accurate revenue modeling and overcome three key obstacles that hinder the
integration of prediction and optimal scheduling. Utilizing an innovative
Pre-schedule-Execute-Re-schedule paradigm and flexible scheduling modes, Seer
achieves efficient revenue-optimized scheduling in CCPs. Extensive evaluations
demonstrate Seer's superiority over competitors in terms of revenue,
utilization, and anomaly penalty mitigation, boosting CCPs revenue by 147% and
expediting scheduling $3.4 \times$ faster.
