Recent years have witnessed video streaming grad- ually evolve into one of the most popular Internet applications. With the rapidly growing personalized demand for real-time video streaming services, maximizing their Quality of Experience (QoE) is a long-standing challenge. The emergence of the server- less computing paradigm has potential to meet this challenge through its fine-grained management and highly parallel comput- ing structures. However, it is still ambiguous how to implement and configure serverless components to optimize video streaming services. In this paper, we propose EAVS, an Edge-assisted Adaptive Video streaming system with Serverless pipelines, which facilitates fine-grained management for multiple concurrent video transmission pipelines. Then, we design a chunk-level optimiza- tion scheme to address video bitrate adaptation. We propose a Deep Reinforcement Learning (DRL) algorithm based on Proximal Policy Optimization (PPO) with a trinal-clip mechanism to make bitrate decisions efficiently for better QoE. Finally, we implement the serverless video streaming system prototype and evaluate the performance of EAVS on various real-world network traces. Our results show that EAVS significantly improves QoE and reduces the video stall rate, achieving over 9.1% QoE improvement and 60.2% latency reduction compared to state- of-the-art solutions.