Review of content‐aware resource allocation schemes for video streaming over wireless networks

Pahalawatta, Peshala V.; Katsaggelos, Aggelos K.

doi:10.1002/wcm.469

Cited by 26 publications

(11 citation statements)

References 30 publications

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“…Video streaming over mesh networks is a challenging task because compressed videos are very sensitive to transmission errors (e.g., packet loss), video transmission has stringent delay requirements, and video sessions compete with each other for limited network resources to maximize their quality of service [3], [4]. The ultimate purpose of video compression and network transmission is to provide the best video presentation quality for end users.…”

Section: Introductionmentioning

confidence: 99%

Multihop Packet Delay Bound Violation Modeling for Resource Allocation in Video Streaming Over Mesh Networks

Zhang

Sun

et al. 2010

IEEE Trans. Multimedia

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Resource allocation plays a critical role in multisession video streaming over mesh networks to maximize the overall video presentation quality under transmission delay and network resource constraints. A critical component in efficient resource allocation is to analyze and model the multihop queuing behavior along the transmission path, estimate the packet loss ratio due to delay bound violation, and predict the amount of video quality degradation after multihop video transmission. In this work, we develop a multihop packet delay bound violation model to predict the packet loss probability and end-to-end distortion for video streaming over multihop networks. To this end, we extract salient features to characterize the input source and network conditions of links along the transmission path and construct a learning-based model using artificial neural network (ANN). Based on this model, we then formulate the resource allocation into a nonconvex optimization problem which aims to minimize the overall video distortion while maintaining fairness between sessions. We solve this optimization problem using Lagrangian duality methods. Extensive experimental results demonstrate that, with this widely-used offline-training-online-estimation mechanism, the proposed model is potentially applicable to almost all network conditions and can provide fairly accurate estimation results as compared with other models with a given sample data set. The proposed optimization algorithm achieves more efficient resource allocation than existing schemes.Index Terms-Multihop mesh networks, networks optimization, resource allocation, video streaming.

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Section: Introductionmentioning

confidence: 99%

Multihop Packet Delay Bound Violation Modeling for Resource Allocation in Video Streaming Over Mesh Networks

Zhang

Sun

et al. 2010

IEEE Trans. Multimedia

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“…In fact, existing wireless networks provide dynamically varying resources with only limited support for the quality of service required by delay-sensitive, bandwidth-intense, and loss-tolerant multimedia applications. One of the key challenges associated with multimedia transmission over wireless networks is the timevarying nature of the characteristics of transmitted contents and of the wireless channel [1,2].…”

Section: Introductionmentioning

confidence: 99%

Cross-layer optimization of a multimedia streaming system via dynamic programming

Combernoux

Delestre

Changuel³

et al. 2012

2012 19th IEEE International Conference on Image Processing

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To cite this version:Alice Combernoux, Cyrile Delestre, Nesrine Changuel, Bessem Sayadi, Michel Kieffer. Cross-layer optimization of a multimedia streaming system via dynamic programming. The problem is cast in the framework of Markov Decision Processes (MDP). This formalism provides efficient tools to compute a foresighted control policy maximizing some longterm discounted sum of rewards linked to the video quality received by the user. Experimental results illustrate the benefits in terms of average PSNR of this approach compared to a short-term (myopic) policy. The robustness of the proposed control policy to variations of the transmitted contents is also illustrated.

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“…Lastly, functionalities handled at different layers of the communication protocol stack are inherently and strictly coupled due to the shared nature of the communication channel. Hence, different functionalities aimed at QoS provisioning should not be treated separately when efficient solutions are sought, i.e., a cross-layer design approach is needed [5], [6], [7], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

A distortion-minimizing rate controller for wireless multimedia sensor networks

Pudlewski

Melodia

2010

Computer Communications

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Abstract-The availability of inexpensive CMOS cameras and microphones that can ubiquitously capture multimedia content from the environment is fostering the development of Wireless Multimedia Sensor Networks (WMSNs), i.e., distributed systems of wirelessly networked devices that can retrieve video and audio streams, still images, and scalar sensor data. WMSNs require the sensor network paradigm to be re-thought in view of the need for mechanisms to deliver multimedia content with a pre-defined level of quality of service (QoS).A new rate control scheme for WMSNs is introduced in this paper with a two-fold objective: i) maximize the video quality of each individual video stream; ii) maintain fairness in video quality between different video streams. The rate control scheme is based on both analytical and empirical models and consists of a new cross-layer control algorithm that jointly regulates the end-to-end data rate, the video quality, and the strength of the channel coding at the physical layer. The end-to-end data rate is regulated to avoid congestion while maintaining fairness in the domain of video quality rather than data rate. Once the endto-end data rate has been determined, the sender adjusts the video encoder rate and the channel encoder rate based on the overall rate and the current channel quality, with the objective of minimizing the distortion of the received video. Simulations show that the proposed algorithm considerably improves the received video quality without sacrificing fairness.

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Review of content‐aware resource allocation schemes for video streaming over wireless networks

Cited by 26 publications

References 30 publications

Multihop Packet Delay Bound Violation Modeling for Resource Allocation in Video Streaming Over Mesh Networks

Multihop Packet Delay Bound Violation Modeling for Resource Allocation in Video Streaming Over Mesh Networks

Cross-layer optimization of a multimedia streaming system via dynamic programming

A distortion-minimizing rate controller for wireless multimedia sensor networks

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