With the increasing video demand in daily network traffic, it is an urgent task to develop effective algorithms to facilitate high-quality content delivery service. Recently, numerous adaptive streaming algorithms have been proposed to improve the user perceived experience. We tested the performance of current adaptation methods in multi-user network. It is found that current algorithms perform inconsistently in various network scenarios. In the excessive user and limited bandwidth cases, machine learning and scheduling techniques show superiority in providing high and equal QoE for all users. While in the high-delay case, the buffer-based approaches show robust performance. We also proposed a new client-side ABR control method, flexible relaxation assisted by buffer (FRAB), to achieve fair, stable and efficient video streaming among different users. FRAB is evaluated in real experiments under different network conditions and compared with conventional multi-user ABR algorithms. The experiment results demonstrated that the proposed method has superior performances in multi-user DASH video streaming.