Cross-Layer Design for Real-Time Video Transmission in Cognitive Wireless Networks

Abstract

Cognitive Radio (CR) technologies have been proposed as a promising solution to improve connectivity, selfadaptability, and efficiency of spectrum usage by using dynamic spectrum resource management. When used in multimedia applications in wireless networks, the end-to-end user-perceived video quality experienced by secondary users is a very important performance metric to evaluate the effectiveness of CR technologies. However, most of the current research activities consider only spectrum utilization and effectiveness, ignoring the system performance of upper layers. In this paper, we propose an end-to-end system to optimize the user-perceived video quality at the receiver end under the constraint of packet delay bound in cognitive wireless networks. By mathematically formulating the design objective and the design constraint, the impact of inextricably interrelated system parameters residing in different network layers are jointly considered and optimized in a holistic way to achieve the best-possible user-perceived video quality for secondary users. Further, we formulate the problem into a MIN-MAX problem, which is then solved by using dynamic programming. The performance enhancement by using the proposed system is demonstrated through extensive experiments based on H.264/AVC.

Multimedia Communication over Cognitive Radio Networks from QoS/QoE Perspective; A Comprehensive Survey

“…Closed-form Expression [132] Column Generationbased Algorithm [85] Dynamic Programming [135], [206], [229], [239], [240] Fountain Code [125], [126], [201] Greedy Algorithm [86]- [89], [100], [106], [207], [216], [241]- [243] Non-linear Programming [136], [244], [245] Lift-and-Project [246] Miscellaneous Multi-channel Mode [86], [87], [105], [137], [145], [146], [242], [247]- [250] Carrier Aggregation [84], [ [78], [83], [84], [100], [103], [121], [134], [137], [200], [209], [247], [249], [257]- [259] fully cognitive, they need to be equipped with learning and reasoning capabilities. Us...…”

“…Using S-ALOHA, the closed form result of latency and the throughput-based utility function was derived. [240] proposed a cross-layer end-to-end system to optimize the QoE by considering packet delay bound in CRNs for real-time video transmission. By designing the objective function and constraints based on the interactions among various network function, the video quality for SUs is improved considerably in terms of PSNR.…”

“…The authors in [84], [135], [202], [205], [229], [230], [230], [234], [236], [240] adopted a TDMA multi-user CRN, where at the beginning of every time slot, each user tried to access the channel after a certain time delay and tried to minimize the delay by restricting the input bitrate to the video BL. The authors in [135], [240] considered the issue of realtime wireless video transmission over CRNs and designed a distortion-delay optimization problem based on the encoder behavior, cognitive MAC scheduling, transmission and modulation, and the coding scheme in order to achieve the best user-perceived video quality. The problem was solved using dynamic programming.…”

Window‐based adaptive technique for real‐time streaming of scalable video over cognitive radio networks

“…Another cross‐layer approach, that takes into considerations the queue status as well as the channel quality for the users to allocate the available resources while satisfying their real‐time requirements, is introduced in [3]. In [4, 5], the CR channel condition was used to minimise the video distortion of the SUs for real‐time non‐scalable video transmission. Real‐time video streaming has also been investigated in [6], where a resource allocation algorithm was proposed for an orthogonal frequency‐division multiple access uplink case to achieve the SUs real‐time requirements.…”