Motivated by emerging vision-based intelligent services, we consider the problem of rate adaptation for high quality and low delay visual information delivery over wireless networks using scalable video coding. Rate adaptation in this setting is inherently challenging due to the interplay between the variability of the wireless channels, the queuing at the network nodes and the frame-based decoding and playback of the video content at the receiver at very short time scales. To address the problem, we propose a low-complexity, model-based rate adaptation algorithm for scalable video streaming systems, building on a novel performance model based on stochastic network calculus. We validate the model using extensive simulations. We show that it allows fast, near optimal rate adaptation for fixed transmission paths, as well as cross-layer optimized routing and video rate adaptation in mesh networks, with less than @math quality degradation compared to the best achievable performance.
Stochastic network calculus has been extended to capture the randomly varying channel capacity of wireless links, following different methods @cite_7 @cite_29 @cite_4 @cite_13 @cite_14 @cite_40 . Most of the existing work builds on an abstracted finite-state Markov channel (FSMC) model of the underlying fading channel, e.g., @cite_29 @cite_4 or uses moment generating function based network calculus @cite_40 . However, the complexity of the resulting models limits the applicability of these approaches in multi--hop wireless network analysis with more than a few state FSMC model and more than two hops. In this work, we follow the approach proposed by Al- @cite_18 , where a wireless network calculus based on the dioid algebra was developed. The main premise for this approach is that the channel capacity, and hence the offered service of fading channels is related to the instantaneous received SNR through the logarithmic function as expressed by the Shannon capacity, @math . Hence, an equivalent representation of the channel capacity in an isomorphic transform domain, obtained using the exponential function, would be @math . This simplifies the otherwise cumbersome computations of the end-to-end performance metrics.
@cite_18 @cite_14 @cite_4 @cite_7 @cite_29 @cite_40 @cite_13
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The MIMO wireless channel offers a rich ground for quality of service analysis. In this work, we present a stochastic network calculus analysis of a MIMO system, operating in spatial multiplexing mode, using moment generating functions (MGF). We quantify the spatial multiplexing gain, achieved through multiple antennas, for flow level quality of service (QoS) performance. Specifically we use Gilbert-Elliot model to describe individual spatial paths between the antenna pairs and model the whole channel by an @math -State Markov Chain, where @math depends upon the degrees of freedom available in the MIMO system. We derive probabilistic delay bounds for the system and show the impact of increasing the number of antennas on the delay bounds under various conditions, such as channel burstiness, signal strength and fading speed. Further we present results for multi-hop scenarios under statistical independence. The class of Gupta-Kumar results, which predict the throughput capacity in wireless networks, is restricted to asymptotic regimes. This tutorial presents a methodology to address a corresponding non-asymptotic analysis based on the framework of the stochastic network calculus, in a rigorous mathematical manner. In particular, we derive explicit closed-form results on the distribution of the end-to-end capacity and delay, for a fixed source-destination pair, in a network with broad assumptions on its topology and degree of spatial correlations. The results are non-asymptotic in that they hold for finite time scales and network sizes, as well as bursty arrivals. The generality of the results enables the research of several interesting problems, concerning for instance the effects of time scales or randomness in topology on the network capacity. Network calculus is an established theory for deterministic quality of service analysis of fixed networks. Due to the failures inherent in fading channels it is, however, not applicable to radio systems. Emerging probabilistic equivalents allow closing this gap. Based on the recent network calculus with moment generating functions we present a methodology for performance analysis of fading channels. We use a service curve representation of radio links which facilitates an efficient analysis of radio networks. We investigate fading channels with memory and our results show that the fading speed impacts service guarantees significantly. Numerical performance bounds are provided for an example taken from cellular radio communications for which the effects of opportunistic scheduling are quantified. Simulation results are shown which confirm the efficiency of the approach. Network calculus is a min-plus system theory for performance evaluation of queuing networks. Its elegance steins from intuitive convolution formulas for concatenation of deterministic servers. Recent research dispenses with the worst-case assumptions of network calculus to develop a probabilistic equivalent that benefits from statistical multiplexing. Significant achievements have been made, owing for example to the theory of effective bandwidths; however, the outstanding scalability set up by concatenation of deterministic servers has not been shown. This paper establishes a concise, probabilistic network calculus with moment generating functions. The presented work features closed-form, end-to-end, probabilistic performance bounds that achieve the objective of scaling linearly in the number of servers in series. The consistent application of moment generating functions put forth in this paper utilizes independence beyond the scope of current statistical multiplexing of flows. A relevant additional gain is demonstrated for tandem servers with independent cross-traffic In this paper, we develop a method for analyzing time-varying wireless channels in the context of the modern theory of the stochastic network calculus. In particular, our technique is applicable to channels that can be modeled as Markov chains, which is the case of channels subject to Rayleigh fading. Our approach relies on theoretical results on the convergence time of reversible Markov processes and is applicable to chains with an arbitrary number of states. We provide two expressions for the delay tail distribution of traffic transmitted over a fading channel fed by a Markov source. The first expression is tighter and only requires a simple numerical minimization, the second expression is looser, but is in closed form.