This paper presents a novel Q-learning based auction (QL-BA) algorithm for dynamic spectrum access in a one primary user multiple secondary users (OPMS) scenario. In the auction market, the secondary user provides a bidding price dynamically and intelligently using a Q-learning based bidding strategy to compete for current access opportunity; meanwhile primary user decides to whom to release the unused spectrum according to the maximal bidding principle. To obtain the limited and time-varying spectrum opportunities, each bidder presents a preference utility through Q-learning, considering the current packet transmission and future expectation. Simulation results show that the proposed QL-BA can significantly improve secondary users' bidding strategies and, hence, the performance in terms of packet loss, bidding efficiency and transmission rate is improved progressively.

We investigate a multi-user multi-service scheduling scheme in the OFDMA based two-hop cooperative relay network, which aims at the cooperative resource allocation in this paper. We propose an adaptive Aggregated Utility Function (AU-Function) as the optimization objective, which simultaneously takes various multi-service QoS requirement and fairness among different services into consideration. Weformat the utility function into a several QoS parameters captured form, including rate, delay, jitter and Packet Loss Ratio (PLR) of services, which combines the QoS requirement well. Then, the complex resource allocation is decoupled into a joint relay-subcarrier selection and power allocation problem. Simulation results confirm that the proposed algorithm achieves an efficient balance among rate, delay, PLR, etc., and show that the users' QoS can be evaluated adaptively by the full dimensional utility consideration.

The bit stream extraction plays an important role in Scalable Video Coding (SVC) [1]. However, one downside of current video coding methods is to ignore the video contents which is in fact an important factor for video coding efficiency. Therefore, an equivalent MSE method is proposed in this paper to extract substreams in the temporal and spatial enhancement layers. When the Motion Vectors (MVs) are large in one video, a larger frame rate is necessary to maintain the continuity of the object movement which makes no jump in the visual sense. In this sense, substreams extraction in temporal enhancement layer has to be satisfied. On the other hand, if there are some larger high-frequency components in a single frame of the video, that is to say, there are some higher spatial details in the video stream. As a result, it should try to meet the extraction requirement in spatial enhancement layer. This method has the advantage of considering the contents of the video, which can effectively improve the coding performance and quality. The experimental results have demonstrated the improved quality of reconstructed video for the equivalent MSE method when extracting bit stream arbitrarily at the same bandwidth.

In this paper, we propose a cross-layer protocol that is based on a form of cooperative transmission called Virtual Multi-Input Single-Output (VMISO). Specifically, we discuss the influence of rate, cluster size, and transmission range of the VMISO link on the whole performance improvements, where the key physical layer property that we focus on is an enlarged transmission range due to cooperative diversity. Further, the new protocol, termed the Joint Cross-layer Cooperative Transmission Protocol (JCCTP) including the VMISO routing protocol and the novel MAC protocol, presents the distributed approach of using VMISO links with a fixed cluster size, a given rate, and a certain transmission range, where we leverage the translation of physical layer advantages into higher layer better performances. Finally, we evaluate JCCTP with a flat Rayleigh fading channel model that accurately captures the nature of VMISO transmissions. As compared to using only SISO links and other VMISO links, our work achieves a great increase in the end-to-end throughput and a sharp decline in the end-to-end delay.