Generalized Nash Bargaining Solution to Rate Control Optimization for Spatial Scalable Video Coding

Rate control (RC) optimization is indispensable for scalable video coding (SVC) with respect to bitstream storage and video streaming usage. From the perspective of centralized resource allocation optimization, the inner-layer bit allocation problem is similar to the bargaining problem. Therefore, bargaining game theory can be employed to improve the RC performance for spatial SVC. In this paper, we propose a bargaining game-based one-pass RC scheme for spatial H.264/SVC. In each spatial layer, the encoding constraints, such as bit rates, buffer size are jointly modeled as resources in the inner-layer bit allocation bargaining game. The modified rate-distortion model incorporated with the inter-layer coding information is investigated. Then, the generalized nash bargaining solution (NBS) is employed to achieve an optimal bit allocation solution. The bandwidth is allocated to the frames from the generalized NBS adaptively based on their own bargaining powers. Experimental results demonstrate that the proposed RC algorithm achieves appealing image quality improvement and buffer smoothness. The average mismatch of our proposed algorithm is within the range of 0.19%-2.63%.