Real-time Dash Streaming Architecture for Internet of Things Using FBMRWP Model for Medical Videos

Abstract

Background: The proposed method uses random adjustments in the online video quality based on the bandwidth allocated over Dynamic Adaptive Streaming over HTTP (DASH) streaming service. Aim: The main objective is to improve the video quality from DASH-HTTP servers with variable bandwidth. Here, the system is adjusted dynamically for providing best video quality services based on the requirement of the user. Methods: In order to achieve such objective, the DASH service is assigned with three modules. Initially, the quality is adjusted dynamically using Fractional Brownian Motion and Random Waypoint Mobility (FBM-RWP) model. This initial model schedules the packets in sub-streams based on the priority as per the requirement of the user. The final model uses the Proportional Integral Derivative (PID) quality control algorithm for the past and future prediction of quality based on bandwidth allocation. This feedback of quality is used by the FBM-RWP model to prioritize the packets in the sub-streams. The entire process works by matching the bit rate of video streaming with the user required quality. Results: The technique concentrates mostly on medical videos for improving the live video streaming in case of medical emergencies. The performance of the proposed method is compared with the conventional DASH services. The results proved that the proposed method performs better in terms of reduced error and improved throughput.