Reference Frame Optimization for Multiple-Path Video Streaming With Complexity Scaling

Recent video coding standards such as H.264 offer the flexibility to select reference frames during motion estimation for predicted frames. In this paper, we study the optimization problem of jointly selecting the best set of reference frames and their associated transport QoS levels in a multipath streaming setting. The application of traditional Lagrangian techniques to this optimization problem suffers from either bounded worst case error but high complexity or low complexity but undetermined worst case error. Instead, we present two optimization algorithms that solve the problem globally optimally with high complexity and locally optimally with lower complexity. We then present rounding methods to further reduce computation complexity of the second dynamic programming-based algorithm at the expense of degrading solution quality. Results show that our low-complexity dynamic programming algorithm achieves results comparable to the optimal but high-complexity algorithm, and that gradual tradeoff between complexity and optimization quality can be achieved by our rounding techniques