Dynamic video rate adaptation based on pre-congestion notification

Multimedia services such as Video on Demand and network-based personal video recording introduce important new management challenges to network and service providers. Given the high revenue opportunities of these services, it is important to maximize the Quality of Experience (QoE) of multimedia services as much as possible. Traditionally, admission control mechanisms are used to protect the QoE of existing resources and to avoid that the traffic rate on a link exceeds a predefined threshold. Using admission control, flows are blocked when congestion is imminent. For video based services, the traffic rate can also be controlled by switching existing flows to a lower video quality. In this case, the videos can still be viewed but at a reduced QoE, which increases the available resources and thus makes room for new flows. In this paper, we focus on the video rate adaptation process. We propose a distributed video rate adaptation algorithm that allows controlling which qualities are offered to the users and how the videos are adapted as a response to changes in the network load. The video rate adaptation algorithm uses the information available in the Pre-Congestion Notification mechanism, a measurement based admission control mechanism standardized recently by the IETF. The video rate adaptation process is steered by utility functions, which define how the quality of the videos should be adapted as a function of the network load.