Providing quality-of-service (QoS) to video delivery in wireless networks has attracted intensive research over the years. A fundamental problem in this area is how to map QoS criterion at different layers and optimize QoS across the layers. In this paper, we investigate this problem and present a cross-layer mapping architecture for video transmission in wireless networks. There are several important building blocks in this architecture, among others, QoS interaction between video coding and transmission modules, QoS mapping mechanism, video quality adaptation, and source rate constraint derivation. We describe the design and algorithms for each building block, which either builds upon or extend the state-of-the-art algorithms that were developed without much considerations of other layers. Finally, we use simulation results to demonstrate the performance of the proposed architecture for progressive fine granularity scalability video transmission over time-varying and nonstationary wireless channel.
The Content-Centric Networking (CCN) architecture exploits a universal caching strategy whose inefficiency has been confirmed by research communities. Various caching schemes have been proposed to overcome some drawbacks of the universal caching strategy but they come with additional complexity and overheads. Besides those sophisticated caching schemes, there is a probabilistic caching scheme that is more efficient than the universal caching strategy and adds a modest complexity to a network. The probabilistic caching scheme was treated as a benchmark and the insights into its behavior have never been studied despite its promising performance and feasibility in practical use. In this paper we study the probabilistic caching scheme by means of computer simulation to explore the behavior of the probabilistic caching scheme when it works with various cache replacement policies. The simulation results show the different behavioral characteristics of the probabilistic caching scheme as a function of the cache replacement policy.
A novel adaptive mapping from the measuremenis of a non-statibnary wireless environment to a variable length Markov chain (VLMC) model is proposed in this research. This scheme consists of two main components: the estimation of channel signal-to-noise ratio (SNR) distribution and discrete VLMC modeling. To obtain the channel SNR distribution, a kernel density estimation algorithm is used to track local changes of channel statistics resulting from varying mobile environments. With the estimated channel SNR distribution, an iterative partitioning mechanism is performed to construct the VLMC model, which. yields a much larger and structurally richer class of models than ordinary higher order Markov chains. The application of the derived VLMC channel model to the available throu,ghput of a non-stationary wireless channel is examined with the feedback channel state information from the mobile terminal to the base station. The performance ,of the proposed adaptive mapping scheme and throughput estimation is demonstrated via simulation in a micro-cell non-stationary wireless environment.
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