We study the important problem of resource allocation for the downlink of Multiple-Input Multiple output (MIMO) Multicast Wireless Systems operating over frequency-selective channels and we propose a low-complexity but efficient resource allocation algorithm for MIMO-enabled OFDMA systems.  The proposed solution guarantees a minimum spectrum share for each user while also takes advantage of the multicast transmission mode. The presence of multiple antennas in both transmitter and receiver offers spatial diversity to the system along with the frequency diversity added by the OFDMA access scheme. The computational complexity is reduced from exponential to linear and validation of the proposed solution is achieved through various simulation scenarios in comparison with other multicast and unicast reference schemes used in MIMO-OFDMA systems. Numerical results and complexity analysis demonstrate the feasibility of the proposed algorithm.

Ioannis G. Fraimis

Stavros A. Kotsopoulos

A Low-Complexity Resource Allocation Algorithm for MIMO-OFDMA Multicast Systems with Spectrum-Guarantee Provisioning

Yifeng He

Ling Guan

Wenwu Zhu

Ivan Lee

Cloud Computing and Dynamic Resource Allocation for Multimedia Applications

Multi-channel Peer-to-Peer (P2P) Video-on-Demand
(VoD) systems can be categorized into independent-channel P2P
VoD systems and correlated-channel P2P VoD systems. Streaming
capacity for a channel is defined as the maximal streaming
rate that can be received by every user of the channel. In this
paper, we study the streaming capacity problem in multi-channel
P2P VoD systems. In an independent-channel P2P VoD system,
there is no resource correlation among channels. Therefore, we
can find the average streaming capacity for the independent-channel
P2P VoD system by finding the streaming capacity for
each individual channel, respectively. We propose a distributed
algorithm to solve the streaming capacity problem for a single
channel in an independent-channel P2P VoD system. The average
streaming capacity for a correlated-channel P2P VoD system
depends on both the intra-channel and cross-channel resource
allocation. To better utilize the cross-channel resources, we
first optimize the server upload allocation among channels to
maximize the average streaming capacity and then propose
cross-channel helpers to enable cross-channel sharing of peer
upload bandwidths. We demonstrate in the simulations that the
correlated-channel P2P VoD systems with both intra-channel and
cross-channel resource allocation can obtain a higher average
streaming capacity compared to the independent-channel P2P
VoD systems with only intra-channel resource allocation.

Improving Streaming Capacity in Multi-Channel
P2P VoD Systems via Intra-Channel and
Cross-Channel Resource Allocation

This paper deals with radio resource allocation in fourth generation (4G) wireless mobile networks based on Orthogonal Frequency Division Multiple Access (OFDMA) as
an access method. In IEEE 802.16 m standard, a contiguous method for subchannel construction is adopted in order to reduce OFDMA system complexity. In this context, we propose a new subchannel gain computation method depending on frequency responses dispersion. This method has a crucial role in the resource management and optimization. In a single service access, we propose a dynamic resource allocation algorithm at the physical layer aiming to maximize the cell data rate while ensuring fairness among users. In heterogeneous data traffics, we study scheduling in order to provide delay guaranties to real-time services, maximize throughput of non-real-time services while ensuring fairness to users. We compare performances to recent existing algorithms in OFDMA systems showing that proposed schemes provide lower complexity, higher total system capacity, and fairness among users.

Wafa Ben Hassen

Meriem Afif

A Gain-Computation Enhancements Resource Allocation for Heterogeneous Service Flows in IEEE 802.16 m Mobile Networks

The increasing popularity of network-based multimedia applications poses many challenges for content providers to supply efficient and scalable services. Peer-to-peer (P2P) systems have been shown to be a promising approach to provide large-scale video services over the Internet since, by nature, these systems show high scalability and robustness. In this paper, we propose and analyze an object management policy approach for video web cache in a P2P context, taking advantage of object&#39;s metadata, for example, video popularity, and object&#39;s encoding techniques, for example, scalable video coding (SVC). We carry out trace-driven simulations so as to evaluate the performance of our approach and compare it against traditional object management policy approaches. In addition, we study as well the impact of churn on our approach and on other object management policies that implement different caching strategies. A YouTube video collection which records over 1.6 million video&#39;s log was used in our experimental studies. The experiment results have showed that our proposed approach can improve the performance of the cache substantially. Moreover, we have found that neither the simply enlargement of peers&#39; storage capacity nor a zero replicating strategy is effective actions to improve performance of an object management policy.

Dario Vieira

Cesar A. V. Melo

Yacine Ghamri-Doudane

Performance Evaluation of an Object Management Policy Approach for P2P Networks

Task assignment in grid computing, where both processing and bandwidth constraints at multiple heterogeneous devices need to be considered, is a challenging problem. Moreover, targeting the optimization of multiple objectives makes it even more challenging. This paper presents a task assignment strategy based on genetic algorithms in which multiple and conflicting objectives are simultaneously optimized. Specifically, we maximize task execution quality while minimizing energy and bandwidth consumption. Moreover, in our video processing scenario; we consider transcoding to lower spatial/temporal resolutions to tradeoff between video quality; processing, and bandwidth demands. The task execution quality is then determined by the number of successfully processed streams and the spatial-temporal resolution at which they are processed. The results show that the proposed algorithm offers a range of Pareto optimal solutions that outperforms all other reference strategies.

Carolina Blanch Perez del Notario

Rogier Baert

Maja D&#39;Hondt

Multi-Objective Genetic Algorithm for Task Assignment on Heterogeneous Nodes

The emergence of cloud encoding services facilitates many content owners, such as the online video vendors, to transcode their digital videos without infrastructure setup. Such service provider charges the customers only based on their resource consumption. For both the service provider and customers, lowering the resource consumption while maintaining the quality is valuable and desirable. Thus, to choose a cost-effective encoding parameter, configuration is essential and challenging due to the tradeoff between bitrate, encoding speed, and resulting quality. In this paper, we explore the feasibility of an automatic parameter-tuning framework, based on which the above objective can be achieved. We introduce a simple service model, which combines the bitrate and encoding speed into a single value: encoding cost. Then, we conduct an empirical study to examine the relationship between the encoding cost and various parameter settings. Our experiment is based on the one-pass Constant Rate Factor method in x264, which can achieve relatively stable perceptive quality, and we vary each parameter we choose to observe how the encoding cost changes. The experiment results show that the tested parameters can be independently tuned to minimize the encoding cost, which makes the automatic parameter-tuning framework feasible and promising for optimizing the cost on video encoding cloud.

Xiaowei Li

Yi Cui

Yuan Xue

Towards an Automatic Parameter-Tuning Framework for Cost Optimization on Video Encoding Cloud

Internet television (IPTV) is rapidly gaining popularity and is being widely deployed in content delivery networks on the Internet. In order to proactively deliver optimum user quality of experience (QoE) for IPTV, service providers need to identify network bottlenecks in real time. In this paper, we develop psycho-acoustic-visual models that can predict user QoE of multimedia applications in real time based on online network status measurements. Our models are neural network based and cater to multi-resolution IPTV applications that include QCIF, QVGA, SD, and HD resolutions encoded using popular audio and video codec combinations. On the network side, our models account for jitter and loss levels, as well as router queuing disciplines: packet-ordered and time-ordered FIFO. We evaluate the performance of our multi-resolution multimedia QoE models in terms of prediction characteristics, accuracy, speed, and consistency. Our evaluation results demonstrate that the models are pertinent for real-time QoE monitoring and resource adaptation in IPTV content delivery networks.

Prasad Calyam

Prashanth Chandrasekaran

Gregg Trueb

Nathan Howes

Rajiv Ramnath

Delei Yu

Ying Liu

Lixia Xiong

Daoyan Yang

Multi-Resolution Multimedia QoE Models for IPTV Applications

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International Journal of Digital Multimedia Broadcasting provides a forum for engineers and researchers whose interests are in digital multimedia broadcasting to share recent developments and challenges in order to design new and improved systems.

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International Journal of Digital Multimedia Broadcasting - Table of contents 2012

International Journal of Digital Multimedia Broadcasting | Hindawi

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International Journal of Digital Multimedia Broadcasting provides a forum for engineers and researchers whose interests are in digital multimedia broadcasting to share recent developments and challenges in order to design new and improved systems.

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International Journal of Digital Multimedia Broadcasting

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International Journal of Digital Multimedia Broadcasting