In order to meet the dramatic wireless bandwidth demands of emerging multimedia applications, cognitive radio has been proposed as one of promising solutions to improve the spectrum efficiency. This paper aims at pursuing high spectrum efficiency via accessing the idle spectrum intelligently without information exchange among users. Different from infrastructure-based wireless networks, users in cognitive radio networks tend to compete with each other to access limited idle spectrum, thus leading to a dynamically heterogeneous radio environment. In this paper, a Q-learning based spectrum access scheme is proposed to adaptively allocate multimedia data over multiple idle spectrum holes. Taking into consideration the rigorous delay and throughput performance requirements of multimedia applications, we integrate these two indicators into the definition of reward function in the proposed Q-learning algorithm. The simulation results show that the proposed scheme can quickly converge to a stable state in terms of throughput, power efficiency, and collision probability. Furthermore, the proposed learning rate adjustment strategy makes the performance of the spectrum access algorithm converge the quickest and only consumes 78% time to achieve the targeted collision probability, i.e., 0.1, compared with two other typical parameter adjustment strategies.

The optimal spectrum access strategy is investigated for multi-user and multi-channel scenario in cognitive radio networks. At first, an online learning method based on Dirichlet process is adopted to predict the channel usage based on ACK/NACK feedbacks, which can avoid frequent information exchange among users. Based on the prediction result, the delay performance can be computed when a user transmits certain percentage of multimedia packets on a specific channel. Secondly, the packet delivery ratio (PDR) can also be derived from the prediction result of channel usage which reflects the accessing competition among multiple users. Finally, the quality of service (QoS) of multimedia applications is defined as the joint delay and throughput performances. Moreover, a dynamic spectrum access scheme is proposed to optimize the QoS metrics. The simulation results demonstrate that the QoS and the peak-signalto- noise ratio (PSNR) of the proposed spectrum access algorithm outperform the three existing spectrum access algorithms, i.e., the cognitive cross-layer algorithm, the dynamic learning algorithm, and the dynamic least interference algorithm. The proposed algorithm achieves more than 21.8%, 5.4%, and 3.9% PDR enhancement and over 2.82dB, 1.08dB, and 0.45dB PSNR gains, compared with those three algorithms, given the transmission power as 10, 20, and 30 units, respectively.

The explosive demands for high quality mobile video services have caused heavy overload to the existing cellular networks. Although the small cell has been proposed to alleviate such a problem, the network operators may not be interested in deploying numerous base stations (BSs) due to expensive infras- tructure construction and maintenance. The unmanned aerial vehicles (UAVs) can provide the low-cost and quick deployment, which can support high-quality line-of-sight communications and have become promising mobile BSs. In this paper, we propose a quality-of-experience (QoE)-driven UAV-enabled pseudo-analog wireless video broadcast scheme, which provides mobile video broadcast services for ground users (GUs). Due to limited energy available in UAV, the aim of the proposed scheme is to maximize the minimum peak signal-to-noise ratio (PSNR) of GUs' video reconstruction quality by jointly optimizing the transmission power allocation strategy and the UAV trajectory. Firstly, the reconstructed video quality at GUs is defined under the constraints of the UAV's total energy and motion mechanism, and the proposed scheme is formulated as a complex non- convex optimization problem. Then, the optimization problem is simplified to obtain a tractable suboptimal solution with the help of the block coordinate descent model and the successive convex approximation model. Finally, the experimental results are presented to show the effectiveness of the proposed scheme. Specifically, the proposed scheme can achieve over 0.4dB PSNR gains in terms of GUs' minimum PSNR, compared with the state- of-the-art schemes, e.g., DVB, SoftCast, and SharpCast.

This letter addresses the energy storage maximization issue in the typical wireless networks aided by reconfigurable intelligent surface (RIS), where the joint simultaneous wireless information and power transfer (SWIPT) and mobile edge computing (MEC) are further exploited in a collaborative manner. In particular, by taking energy harvesting and task offloading into account simultaneously, the RIS reflection coefficients and task allocation strategy are optimized jointly subject to quality of service (QoS) constraints. To tackle the non-convex problem, a practical block coordinate descent (BCD) based alternating optimization algorithm is proposed to obtain a near-optimal solution. Numerical results verify that the proposed RIS-aided joint-SWIPT-MEC scheme outperforms other algorithms.

Nowadays, unmanned aerial vehicles (UAVs), empowered with the capability of high-definition image transmission, are used to capture the rapidly changing physical environment by leveraging its high flexibility to reconstruct an immersive realistic virtual environment for metaverse users. In this paper, we consider a novel UAV-assisted image acquisition system where a UAV is dispatched to take off from an initial location to capture real-time images of multiple ground targets and then transfer the captured images back to the ground user for virtual environment reconstruction. We aim to minimize the time for the UAV to complete the image acquisition task by optimizing the three-dimensional UAV trajectory under the constraints of image quality, information causality and energy consumption. To this end, we first formulate the investigated scenario into a mixed integer optimization problem, which, however, is difficult to solve due to the infinite time-varying variables closely coupled with each other. Then, a three-stage progressive algorithm is proposed to obtain an efficient solution to the formulated mixed integer optimization problem, where the constraints of image quality, information causality and energy consumption can be sequentially satisfied. Finally, comprehensive performance evaluation is conducted to verify the effectiveness of the proposed three-stage progressive trajectory design algorithm, and the results show that the proposed algorithm significantly outperforms the benchmark schemes.

Recently, pseudo analog transmission has gained increasing attentions due to its ability to alleviate the cliff effect in video multicast scenarios. The existing pseudo analog systems are optimized under the minimum mean squared error criterion. However, their power allocation strategies do not take the perceptual video quality into consideration. In this paper, we propose a human-perception-based pseudo analog video transmission system named ROIC-Cast, which aims to intelli- gently enhance the transmission quality of the region-of-interest (ROI) parts. Firstly, the classic deep learning based saliency detection algorithm is adopted to decompose the continuous video sequences into ROI and non-ROI blocks. Secondly, an effective compression method is used to reduce the data amount of side information generated by the ROI extraction module. Then, the power allocation scheme is formulated as a convex problem, and the optimal transmission power for both ROI and non-ROI blocks is derived in a closed form. Finally, the simulations are conducted to validate the proposed system by comparing with a few of existing systems, e.g., KMV-Cast, SoftCast, and DAC-RAN. The proposed ROIC-Cast can achieve over 4.1dB peak signal- to-noise ratio gains of ROI compared with other systems, given the channel signal-to-noise ratio as -5dB, 0dB, 5dB, and 10dB, respectively. This significant performance improvement is due to the automatic ROI extraction, high-efficiency data compression as well as adaptive power allocation.