In the Thesis, we propose a mechanism to dynamically adjust video parameters in an intelligent video surveillance network. Whenever there is an alarm or network encounters congestion, we could adjust video parameters including Frames per Second (FPS), Quality, and Picture Size to adapt to network bandwidth. For examples, we can adjust FPS when an alarm exists in the surveillance system; we can adjust the Quality or Picture Size by counting the total number of video packets received per second to obtain a smooth video when network is congested To demonstrate the proposed schemes, we implement these three adjustable parameters, Quality, Picture size, and FPS on a Linux platform. To do this, we establish a new HTTP connection from a client to a camera and then we develop the corresponding control messages issued by the client in order to change the video parameters. In addition, we implement a video recovery mechanism by measuring the differences in arrival time between every packet (referred to as diff). Finally, we observe with our proposed scheme whether the video quality can be smoother under different background traffics. In the video recovery mechanism, we utilize diff to decide whether a higher quality picture should be persisted or downgraded to a lower quality picture to avoid packet loss under network congestion.