Fog computing is an emerging architecture that aims to run applications on multiple devices that lie on a continuum from centralized cloud servers to personal user devices. These architectures allow applications to optimize over the information stored at each type of device and divide their functionalities based on the device capabilities. We demonstrate the benefits of this approach for mobile video streaming. Existing HAS (HTTP adaptive streaming) techniques, used by popular video service providers, often suffer from problems like unstable video quality and suboptimal resource utilization. We find that a lack of coordination prevents both client- and network-side HAS techniques from solving them. However, our fog approach can exploit existing telecommunication APIs, which expose network capabilities to applications, in order to coordinate between the client and network. Our coordinated HAS solution, FLARE, optimizes the total utility of all clients in a cell while maintaining stable video quality and supporting user- and device-specific needs. We implement FLARE on a commodity LTE femtocell and use the implementation to conduct the first comparison of HAS players on an LTE femtocell. By conducting extensive experiments using the ns-3 simulator, we also demonstrate that FLARE (i) enhances the average video bitrate, (ii) achieves stable video quality, and (iii) balances the throughput of simultaneous video and data flows, compared to other representative HAS solutions.