Video Over Wireless: Maximizing Quality under Limited Resources
Bandwidth-intensive video streaming applications occupy an overwhelming fraction of bandwidth-limited wireless network traffic. The explosion of video data traffic necessitates new transmission paradigms at different protocol layers that improve video quality of experience, introduce error resilience, and meet quality-of-service (QoS) requirements. Real-time video specifically demands QoS guarantees such as delay bounds for end-user satisfaction. Due to the inherently stochastic nature of wireless fading channels, deterministic delay bounds are difficult to guarantee. Furthermore, the tolerable delay varies depending on the use case such as live streaming or two-way video conferencing.
Motivated by this, WNCG Alumnus Amin Abdel Khalek and WNCG Professors Robert Heath and Constantine Caramanis proposed algorithms for resource allocation and scheduling that maximize total video quality in the network while guaranteeing delay bounds per user in a statistical sense. They use the concept of effective capacity to provide statistical delay guarantees and the delay bound per user is application-driven. In two recent papers, they derive the resource allocation policy that maximizes the sum video quality and applies to any perceptual video quality metric with concave rate-quality mapping. They show that the structure of the optimal solution is a function of the rate-distortion slope per user and the supported video source rate per unit bandwidth. Further, they extend the resource allocation policy to capture video quality-driven adaptive user-subcarrier assignment in wideband channels which is applicable to 3GPP LTE. They also consider the alternative problem of fairness-based resource allocation whereby the objective is to maximize the minimum video quality across users. Finally, they derive user admission and scheduling policies that enable selecting a maximal user subset such that all selected users can meet their statistical delay requirement. They show that video users with differentiated QoS requirements can achieve similar video quality with vastly different resource requirements. Thus, QoS-aware scheduling and resource allocation enable supporting significantly more users under the same resource constraints.
The research was funded by Intel and Cisco as part of the Video-Aware Wireless Networks (VAWN) program.