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Publication# Network Resource Allocation for Competing Multiple Description Transmissions

Résumé

Providing real-time multimedia services over a best-effort network is challenging due to the stringent delay requirements in the presence of complex network dynamics. Multiple description (MD) coding is one approach to transmit the media over diverse (multiple) paths to reduce the detrimental effects caused by path failures or delay. The novelty of this work is to investigate the resource allocation in a network, where there are several competing MD coded streams. This is done by considering a framework that chooses the operating points for asymmetric MD coding to maximize total quality of the users, while these streams are sent over multiple routing paths. We study the joint optimization of multimedia (source) coding and congestion control in wired networks. These ideas are extended to joint source coding and channel coding in wireless networks. In both situations, we propose distributed algorithms for optimal resource allocation. In the presence of path loss and competing users, the service quality to any particular MD stream could be uncertain. In such circumstances it might be tempting to expect that we need greater redundancy in the MD streams to protect against such failures. However, one surprising aspect of our study reveals that for large number of users who compete for the same resources, the overall system could benefit through opportunistic (hierarchical) strategies. In general networks, our studies indicate that the user composition varies from conservative to opportunistic operating points, depending on the number of users and their network vantage points.

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Wireless adhoc networks consist of users that want to communicate with each other over a shared wireless medium. The users have transmitting and receiving capabilities but there is no additional infrastructure for assisting communication. This is in contrast to existing wireless systems, cellular networks for example, where communication between wireless users heavily relies on an additional infrastructure of base stations connected with a high-capacity wired backbone. The fact that they are infrastructureless makes wireless adhoc networks inexpensive, easy to build and robust but at the same time technically more challenging. The fundamental challenge is how to deal with interference: many simultaneous transmissions have to be accommodated on the same wireless channel when each of these transmissions constitutes interference for the others, degrading the quality of the communication. The traditional approach to wireless adhoc networks is to organize users so that they relay information for each other in a multi-hop fashion. Such multi-hopping strategies face scalability problems at large system size. As shown by Gupta and Kumar in their seminal work in 2000, the maximal communication rate per user under such strategies scales inversely proportional to the square root of the number of users in the network, hence decreases to zero with increasing system size. This limitation is due to interference that precludes having many simultaneous point-to-point transmissions inside the network. In this thesis, we propose a multiscale hierarchical cooperation architecture for distributed MIMO communication in wireless adhoc networks. This novel architecture removes the interference limitation at least as far as scaling is concerned: we show that the per-user communication rate under this strategy does not degrade significantly even if there are more and more users entering into the network. This is in sharp contrast to the performance achieved by the classical multi-hopping schemes. However, the overall picture is much richer than what can be depicted by a single scheme or a single scaling law formula. Nowadays, wireless adhoc networks are considered for a wide range of practical applications and this translates to having a number of system parameters (e.g., area, power, bandwidth) with large operational range. Different applications lie in different parameter ranges and can therefore exhibit different characteristics. A thorough understanding of wireless adhoc networks can only be obtained by exploring the whole parameter space. Existing scaling law formulations are insufficient for this purpose as they concentrate on very small subsets of the system parameters. We propose a new scaling law formulation for wireless adhoc networks that serves as a mathematical tool to characterize their fundamental operating regimes. For the standard wireless channel model where signals are subject to power path-loss attenuation and random phase changes, we identify four qualitatively different operating regimes in wireless adhoc networks with large number of users. In each of these regimes, we characterize the dependence of the capacity on major system parameters. In particular, we clarify the impact of the power and bandwidth limitations on performance. This is done by deriving upper bounds on the information theoretic capacity of wireless adhoc networks in Chapter 3, and constructing communication schemes that achieve these upper bounds in Chapter 4. Our analysis identifies three engineering quantities that together determine the operating regime of a given wireless network: the short-distance signal-to-noise power ratio (SNRs), the long-distance signal-to-noise power ratio (SNRl) and the power path-loss exponent of the environment. The right communication strategy for a given application is dictated by its operating regime. We show that conventional multi-hopping schemes are optimal when the power path-loss exponent of the environment is larger than 3 and SNRs ≪ 0 dB. Such networks are extremely power-limited. On the other hand, the novel architecture proposed in this thesis, based on hierarchical cooperation and distributed MIMO, is the fundamentally right strategy for wireless networks with SNRl ≫ 0 dB. Such networks experience no power limitation. In the intermediate cases, captured by the remaining two operating regimes, neither multi-hopping nor hierarchical-MIMO achieves optimal performance. We construct new schemes for these regimes that achieve capacity. The proposed characterization of wireless adhoc networks in terms of their fundamental operating regimes, is analogous to the familiar understanding of the two operating regimes of the point-to-point additive white Gaussian noise (AWGN) channel. From an engineering point of view, one of the most important contributions of Shannon's celebrated capacity formula is to identify two qualitatively different operating regimes on this channel. Determined by its signal-to-noise power ratio (SNR), an AWGN channel can be either in a bandwidth-limited (SNR ≫ 0 dB) or a power-limited (SNR ≪ 0 dB) regime. Communication system design for this channel has been primarily driven by the operating regime one is in.

In many wired and wireless networks, nodes process input traffic to satisfy a network constraint (e.g., a capacity constraint) and to increase the utility of data in the output flows given these constraints. In this paper we focus on the special case in which data processing is applied to satisfy capacity constraints. This occurs when the sum of the rate of the input traffic at a node exceeds the sum of the capacity of its output links, or in a more general case, when the sum of the input rates is larger than any cut capacity in the network. In this case, nodes process data to decrease the output flow rate. This decrease from input rate to output rate distorts the transmitted data, which we characterize by a distortion metric. We show that the distortion cost of distributively processing input traffic in a network can be written as the sum of the distortion at individual nodes.. We present a distributed algorithm for a data-gathering network with many sources and a data sink that routes traffic and performs in-network data processing to minimize the distortion cost. In this algorithm, each node determines its routing table based on gradient information from neighboring nodes.

2008Adel Aziz, Julien Pierre Sacha Herzen, Ruben Merz, Patrick Thiran

The goal of jointly providing efficiency and fairness in wireless networks can be seen as the problem of maximizing a given utility function. In contrast with wired networks, the capacity of wireless networks is typically time-varying and not known explicitly. Hence, as the capacity region is impossible to know or measure exactly, existing scheduling schemes either under-estimate it and are too conservative, or they over-estimate it and suffer from congestion collapse. We propose a new adaptive algorithm, called Enhance & Explore (E&E). It maximizes the utility of the network without requiring any explicit characterization of the capacity region. E&E works above the MAC layer and it does not demand any modification to the existing networking stack. We first evaluate our algorithm theoretically and we prove that it converges to a state of optimal utility. We then evaluate the performance of the algorithm in a WLAN setting, using both simulations and real measurements on a testbed composed of IEEE 802.11 wireless routers. Finally, we investigate a wireless mesh network setting and we find that, when coupled with an efficient mechanism for congestion control, the E&E algorithm greatly increases the utility achieved by multi-hop networks as well.

2011