In this paper, an LSTM based rate-distortion (R-D) prediction method for low-delay video coding has been proposed. Unlike the traditional rate control algorithms, LSTM is introduced to learn the latent pattern of the R-D relationship in the progress of video coding. Temporal information, hierarchical coding structure information and the content of the frame which is to be encoded have been used to achieve more accurate prediction. Based on the proposed network, a new R-D model parameters prediction method is proposed and tested on test model of Versatile Video Coding (VVC). According to the experimental results, compared with the state-of-the-art method used in VVC, the proposed method can achieve better performance.

The use of a combinatorial auction is believed to be an effective way to distribute spectrum to buyers who have diversified valuations for different spectrum combinations. However, the allocation of spectrum with combinatorial auctions mainly aims at optimizing over certain utility functions, e.g., social welfare, but ignores individual preferences of buyers and sellers, who have incentives to deviate from globally optimal allocation results to improve their own utility. In this paper, we explore the possibility of designing a new stable matching algorithm for combinatorial spectrum allocations. Starkly different from existing efforts on spectrum matching mechanism design, our proposed combinatorial spectrum matching framework not only allows buyers to express preferences towards spectrum combinations (rather than individual channels), but also computes the payment that should be transferred from buyers to sellers. Payment determination, while essential in spectrum exchange, has never been addressed in existing spectrum matching frameworks. We design a novel algorithm to achieve a stable combinatorial spectrum matching and to compute the corresponding payment profiles. We conducted an extensive array of experiments to compare the performance of stable combinatorial spectrum matching with spectrum auctions. It is shown that the combinatorial spectrum matching sacrifices little allocation efficiency in terms of social welfare and spectrum utilization, but achieves a much higher individual buyer utility, which will incentivize buyers to participate and comply with the allocation results.

Achieving constant video quality is an essential optimization task for video coding applications. In this paper, we propose a two-pass rate control algorithm for High Efficiency Video Coding (HEVC) to reduce the fluctuation of video quality. First, we present a GOP-level rate allocation scheme which aims at minimizing distortion variance among GOPs. To derive an appropriate quantization parameter (QP) for each frame, we establish the distortion-quantizer (D-Q) and the rate-quantizer (R-Q) model based on ν-support vector regression. Further, an R-D tradeoff is designed to adjust the frame QP considering the constant quality control and rate contraints. Extensive experimental results show that the proposed algorithm achieves more constant video quality.