On Video Multicast in Cognitive Radio Networks

Hu, Donglin; Mao, Shiwen; Reed, Jeffrey H.

doi:10.1109/infcom.2009.5062147

Cited by 46 publications

(39 citation statements)

References 21 publications

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“…The proposed optimizer is an integer programming problem which is solved by an approximated algorithm. Unlike our work, [34] fails to consider the buffer storage of users which is particularly useful to fight with the channel dynamics.…”

Section: Cross-layer Design For Qos Provision In Cognitive Radio Netwmentioning

confidence: 96%

“…In [34], the CR users are composed of G multicast groups with each having N g users, g = 1, 2, ..., G. The multicast users of each group may reside in different and fixed spectrum bands without the capability of spectrum mobility. The video files are encoded using the layered video coding and broadcasted to multicast groups from the CR base station.…”

Section: Cross-layer Design For Qos Provision In Cognitive Radio Netwmentioning

confidence: 99%

“…Towards this goal, [34] propose an optimizer of the network which exploits three dimensions of the system: coding rate selection, spectrum selection and spectrum sharing. The first issue is to determine the optimal coding rates for broadcasting.…”

Section: Cross-layer Design For Qos Provision In Cognitive Radio Netwmentioning

confidence: 99%

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Channel Allocation for Smooth Video Delivery over Cognitive Radio Networks

Luan

Shen

2010

2010 IEEE Global Telecommunications Conference GLOBECOM 2010

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Video applications normally demand stringent quality-of-service (QoS) for the high quality and smooth video playback at the receiver. Since the network is usually shared by multiple applications with diverse QoS requirements, QoS provisioning is an important and difficult task for the efficient and smooth video delivery. In the context of cognitive radio (CR) networks, as the secondary or unlicensed users share a pool of bandwidth that is temporarily being unused by the primary or licensed users, there is an inevitable interference between the licensed primary users and the unlicensed CR devices. As a result, efficient and smooth video delivery becomes even more challenging as the channel spectrum is not only a precious resource, but also much more dynamic and intermittently available to secondary users.In this thesis, we focus on the provision of guaranteed QoS to video streaming subscribers in CR network. In video streaming applications, a playout buffer is typically deployed at the receiver to deal with the impact of the network dynamics. With different buffer storage, users can have different tolerance to the network dynamics. We exploit this feature for channel allocation in CR network. To this end, we model the channel availability as an on-off process which is stochastically known. Based on the bandwidth capacity and the specific buffer storage of users, we intelligently allocate the channels to maximize the overall network throughput while providing users with the smooth video playback, which is formulated as an optimization framework. Given the channel conditions and the video packet storage in the playout buffer, we propose a centralized scheme for provisioning the superior video service to users. Simulation results demonstrate that by exploiting the playout buffer of users, the proposed channel allocation scheme is robust against intense network dynamics and provides users with the elongated smooth video playback.

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Section: Cross-layer Design For Qos Provision In Cognitive Radio Netwmentioning

confidence: 96%

Section: Cross-layer Design For Qos Provision In Cognitive Radio Netwmentioning

confidence: 99%

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Channel Allocation for Smooth Video Delivery over Cognitive Radio Networks

Luan

Shen

2010

2010 IEEE Global Telecommunications Conference GLOBECOM 2010

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“…For example in [26], Hu et al took the scalable video coding, video rate control, spectrum sensing, dynamic spectrum access, modulation, scheduling, and primary user protection as design factors to solve the video multicast problem and formulate it as mixed integer nonlinear programming (MINLP) problem. In their approach to solve the problem, they determine the optimal partition and modulation-coding (MC) of FGS data and adjust the solution based on the channel sensing feedback.…”

Section: Multicast Transmissionmentioning

confidence: 99%

Video streaming over cognitive radio networks

Dastpak

Liu

Hefeeda

2012

Proceedings of the 4th Workshop on Mobile Video

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In recent years, there has been a tremendous growth in multimedia applications over the wireless Internet. The significant bandwidth requirement for multimedia services has increased the demand for radio spectrum. The scarcity of radio spectrum has challenged the conventional fixed spectrum assignment policy. As a result, cognitive radio emerged as a new paradigm to address the spectrum underutilization problem by enabling users to opportunistically access unused spectrum bands. In this thesis, we propose a framework for video transmission over cognitive radio networks. Our objective is to determine the optimal streaming policy in order to maximize the overall perceived video quality while keeping quality fluctuation at minimum. In our framework, we introduce a channel usage model based on a two-state Markov model and estimate the future busy and idle durations of the spectrum based on past observations. On the basis of this scheme, we formulate the streaming optimization problem under the constraint of the available bandwidth budget so that the optimal number of enhancement layer bits are assigned to each frame. We extend this algorithm for three different optimization levels: frame, GOP and scene. We evaluate our algorithm through extensive trace-driven simulation, and show that it improves the perceived video quality and increases bandwidth utilization.

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“…In [8], a multicast-tree construction protocol was proposed for mobile ad hoc networks. Video multicast in infrastructure cognitive radio networks was studied in [9]. The cognitive base station chooses the multicast channels opportunistically such that the collision probability with the primary system is minimized, and the unused spectrum is exploited as much as possible.…”

Section: Introductionmentioning

confidence: 99%

Assisted-Multicast Scheduling in Wireless Cognitive Mesh Networks

Almasaeid

Kamal

2010

2010 IEEE International Conference on Communications

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Abstract-In this work, we consider the multicast problem in a single cell in a cognitive mesh network. Due to the potential heterogeneity in channel availability among the members of a multicast group(s), the total multicast time could be longer due to transmitting the multicast data over multiple channels. We propose, in this work, an assisted multicast strategy with the objective of minimizing the total multicast time. This assistance is composed of two main activities, first, allowing the receivers in a multicast group to forward the data they have received to other members of the multicast group(s), and second, allowing the transmission of coded (bitwise XORed) packets so that receivers belonging to different multicast groups can decode and extract their data concurrently. We show, in this paper, that the proposed assistance paradigm achieves a considerable reduction in the total multicast time, which in turn increases the system throughput.

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On Video Multicast in Cognitive Radio Networks

Cited by 46 publications

References 21 publications

Channel Allocation for Smooth Video Delivery over Cognitive Radio Networks

Channel Allocation for Smooth Video Delivery over Cognitive Radio Networks

Video streaming over cognitive radio networks

Assisted-Multicast Scheduling in Wireless Cognitive Mesh Networks

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