A live video stream, when encoded and transmitted using a congestion controlled IP flow, experiences a variety of quality of service due to variations in video content activity and bandwidth availability, resulting in frequent oscillations in video quality. By utilizing a reliable metric of perceived quality, we propose a technique for source rate control of real-time live video that maintains a more uniform quality. The method comprises artificial neural networks to generate predictions of the ongoing quality and a fuzzy rate-quality controller that considers properties of human perception of quality in order to provide smooth streaming quality. Experimental results indicate that in the presence of sufficient buffering, the proposed adaptation technique can improve quality stability, while maintaining TCP-friendly transmission.
['Dimitrios Miras', 'Graham Knight']
Smooth quality streaming of live Internet video