The increasing volume of video traffic presents significant challenges to network transmission, while edge computing accelerates video delivery by leveraging caching and computation to optimize content forwarding. However, as edge computing is generally deployed by service providers in a transparent manner, clients cannot perceive edge states, e.g., cache availability, potentially resulting in suboptimal bitrate decisions. This issue persists even with intelligent bitrate selection approaches on the client side, as the inaccurate estimation of network delivery capacity due to edge cache transparency remains unresolved. Meanwhile, single-edge servers or nodes, with limited cache space and computational capacity for a small number of users, can be more effective by aggregating into clusters to better serve users and optimize resource utilization. Therefore, we propose an edge-assisted bitrate re-adaptation scheme (e-BitRead) for adaptive streaming, utilizing neighbor edges to accelerate video deliveries.e-BitReadintroduces three key innovations: (i) it employs a bitrate re-adaptation mechanism that intelligently selects alternative bitrates from edge servers instead of strictly responding with the requested bitrate, (ii) it utilizes collaborative caching across multiple edge servers to expand available bitrate options through coordinated resource sharing, and (iii) it enhances the learning efficiency through joint optimization of network architecture and reward design, which leverages actor-critic structure to fit into multi-edge bitrate adaptation. In experiments with an intelligent client ABR,e-BitReaddemonstrates its superiority by achieving a 1.43x higher hit ratio compared to the baseline, while improving QoE by 1.93x over the scheme without smart bitrate matching and delivering a 33% gain over the single-edge re-adaptation approach.