Game‐based cross‐layer channel allocation with SVC‐encoded multimedia streams in cognitive radio networks

Abstract

The available unlicensed spectrum is increasingly being used by new wireless technologies, but past measurements show that the licensed spectrum is extremely underutilized. To address this issue, the IEEE 802.22 Working Group is developing a novel wireless air interface standard based on cognitive radios (CRs), i.e. IEEE 802.22 wireless regional area networks (WRANs). Moreover, over the last decade wireless multimedia applications have developed rapidly, raising significant concerns about the quality of service (QoS) of multimedia stream transmissions. In particular, the Joint Video Team (JVT) and ITU-T Video Coding Experts Group (VCEG) jointly proposed Scalable Video Coding (SVC) as the next-generation multimedia compression standard. However, the current IEEE 802.22 WRAN draft does not specify QoS mechanisms for SVC-encoded multimedia stream transmission in CR networks. To resolve this problem, we developed a cross-layer channel allocation algorithm (CLCAA) and a novel media access control (MAC) protocol to work with the algorithm. The CLCAA adapts to the characteristics of multimedia traffic and variations of wireless channels by determining the weighting of source-destination pair, which is determined by the deadlines of SVC-encoded multimedia streams, the queuing delay and channel conditions. The CLCAA then allocates transmission opportunities to source-destination pairs based on their weightings and game theory. We also conducted extensive simulations to demonstrate the efficiency of the CLCAA scheme. The simulation results show that the CLCAA scheme not only guarantees QoS for multimedia traffic but also achieves fairness across different streams.

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

“…In the centralized mode [83], [88]- [90], [104], [121], [123], [136], [202]- [207], a centralized entity, such as BS in cellular networks, controls the resources and access procedure. The central managing entity collects local observations from multiple SUs, and decides the accessible channels through some decision fusion rule and informs the SUs about the available channels.…”

“…To overcome the above-stated issues, in [202], a cross-layer resource management scheme was proposed that considered both the latency and transmission priorities of a multi-layer encoded conten; the authors applied game theory in order to achieve optimal resource allocation. The proposed scheme adapts the context of multimedia content and variations of the available channels by specifying the weighting of the source-destination pair that is specified by the deadlines of the encoded video sequences, the queuing delay, the and channel states.…”

Software‐defined network‐based prioritization to avoid video freezes in HTTP adaptive streaming

“…Next, the prioritization algorithm is executed to detect if the current conditions can lead to a freeze (lines [7][8][9][10][11][12][13][14][15][16][17][18][19][20]. The controller first computes an estimate of the segment download time in the best-effort queue estimatedDownloadTimeBe, as the ratio between the requested segment size segSize and the estimated bandwidth per client in the best-effort queue (line 7).…”

“…Each client can request the segment at the most appropriate quality level on the basis of the locally perceived bandwidth. In this way, video playback dynamically changes according to networks to guarantee the quality of service of scalable video coding multimedia streams [10]. Adzic et al suggest to add additional information into the video segments to enhance the quality decision algorithm [11].…”

EDMA-based schemes for cognitive radio systems with channel state information

“…Techniques such as dirty-paper coding, superposition coding, and rate splitting are typically considered. The impact on the Quality of Service of various cognitive radio capabilities has been investigated in [29] using game theory with reference to video streaming. Additionally, architectures for management of cognitive radio systems are proposed in [7] and the impact on signaling load is analyzed in [26].…”