Dynamic Algorithms for Multicast With Intra-Session Network Coding

Maximizing network lifetime of unreliable and resource constrained wireless networks such as mobile ad hoc networks involves optimal allocation of power and shared channels in the network. Existing optimal power and channel allocation mechanisms mainly focus on throughput maximization and energy efficiency but fail to obtain a trade-off between delay and energy consumption in the network. This paper proposes a game theoretic energy efficient approach that mimics a fair non-cooperative game among nodes competing for shared channels and power level. Unlike existing power and rate control mechanisms, the proposed energy conserving adaptive power and rate control (ECAPRC) mechanism takes into consideration the constraints of outage probability and total average delay to deal with stochastic changes in the channel that improves the network performance in terms of throughput, delay and total energy consumption. Proposed super-modular game has a unique convergence point called “Nash Equilibrium” and its existence and uniqueness is proved in the paper. To enhance the convergence rate of proposed ECAPRC approach, an adaptive grey wolf optimizer is employed to deal with delay and energy constraints. Simulation results and their analysis show that proposed ECAPRC approach outperforms existing approaches such as dynamic rate and power allocation algorithm, rate-effective network utility maximization and energy conserving power and rate control in terms of above mentioned network parameters and total utility of the system.

Multi-layer video streaming allows to provide different video qualities to a group of multicast receivers with heterogeneous receive rates. The number of layers received determines the quality of the decoded video stream. For such layered multicast streaming, network coding provides higher capacity than multicast routing. Network coding can be performed within a layer (intra-layer) or across layers (inter-layer), and in general inter-layer coding outperforms intra-layer coding. An optimal solution to a network coded layered multicast problem may require decoding of the network code at interior nodes to extract information to be forwarded. However, decoding consumes resources and introduces delay, which is particularly undesirable at interior nodes (the routers) of the network. In this paper, we thus focus on the inter-layer network coding problem without decoding at interior nodes. We propose a heuristic algorithm for rate allocation and code assignment based on the Edmonds-Karp maximum flow algorithm and perform simulations that show that our algorithm may even outperform other heuristics that do require decoding at interior nodes.

To improve the resource utilization of traditional two-way wireless relay system, an optimal power allocation (OPA) algorithm for a space-time cooperative network coding scheme employing amplify-and-forward protocol (AF-ST-CNC) is proposed. The system performance in terms of outage probability and its approximation in high signal-to-noise (SNR) regimes are first derived, and by minimizing the larger outage probability of the two sources with respect to the characteristic of network coding, optimal power factors are obtained for some special cases are presented. The proposed algorithm only needs the average channel gain information and has low computation complexity, which facilitates the practical implementation. Simulation results show that the proposed AF-ST-CNC with OPA algorithm outperforms the AF-ST-CNC with equal power allocation (EPA) algorithm and the space-time cooperative network coding scheme employing amplify-and-forward protocol (DF-ST-CNC) with OPA algorithm in terms of outage performance.

In this paper, we consider lossy mobile ad hoc networks where the data rate of a given flow becomes lower and lower along its routing path. One of the main challenges in lossy mobile ad hoc networks is how to achieve the conflicting goal of increased network utility and reduced power consumption, while without following the instantaneous state of a fading channel. To address this problem, we propose a cross-layer rate-effective network utility maximization (RENUM) framework by taking into account the lossy nature of wireless links and the constraints of rate outage probability and average delay. In the proposed framework, the utility is associated with the effective rate received at the destination node of each flow instead of the injection rate at the source of the flow. We then present a distributed joint transmission rate, link power and average delay control algorithm, in which explicit broadcast message passing is required for power allocation algorithm. Motivated by the desire of power control devoid of message passing, we give a near-optimal power-allocation scheme that makes use of autonomous SINR measurements at each link and enjoys a fast convergence rate. The proposed algorithm is shown through numerical simulations to outperform other network utility maximization algorithms without rate outage probability/average delay constraints, leading to a higher effective rate, lower power consumption and delay. Furthermore, we conduct extensive network-wide simulations in NS-2 simulator to evaluate the performance of the algorithm in terms of throughput, delay, packet delivery ratio and fairness.

The low power register scheduling and allocation algorithm is concerned with minimum switching activity and low power multiple voltages. The proposed algorithm executes low power scheduling to reduce switching activity using a shut down technique by the creation of a data flow graph (DFG) from the VHDL description. Also, the low power register allocation algorithm determines the minimum register after the life time analysis of all variables. It minimizes the switching activity using a graph coloring technique for low power consumption. Finally, the total power is reduced by using the low power multiple voltage. The proposed algorithm proves the effect through various filter benchmarks to adopt a low power register scheduling and allocation algorithm considering resource constraint at multiple voltage.

We propose a novel network coding (NC) enabled named data networking (NDN) architecture for scalable video delivery. Our architecture utilizes NC in order to address the problem that arises in the original NDN architecture, where optimal use of the bandwidth and caching resources necessitates the coordination of the Interest forwarding decisions. To optimize the performance of the proposed NC-based NDN architecture and render it appropriate for transmission of scalable video, we devise a novel rate allocation algorithm that decides on the optimal rates of Interests sent by clients and intermediate nodes. The flow of Data packets achieved by this algorithm maximizes the average quality of the video delivered to the client population. To support the handling of Interest and Data packets when intermediate nodes perform NC, we introduce the use of Bloom filters, which store efficiently additional information about the Interest and Data packets, and modify accordingly the standard NDN architecture. We also devise an optimized Interest forwarding strategy that implements the target rate allocation. The proposed architecture is evaluated for transmission of scalable video over PlanetLab topologies. The evaluation shows that the proposed scheme exploits optimally the available network resources.

Adopting a cross-layer approach, in this paper we propose an algorithm for joint routing and network coding. The proposed algorithm jointly assigns routes and designs linear network codes over finite fields to achieve the capacity of the network. The algorithm has a dynamic programming approach where a cost function is used to assign weights to all edges in the network. The cheapest flow is chosen subject to certain encoding constraints in order to achieve the network capacity with network coding while minimizing the network complexity. The effectiveness of the algorithm is demonstrated through carefully chosen examples. We show that the constraints imposed by the joint routing and coding algorithm are necessary for successful decoding at the sinks, and their violation can lead to a failure in achieving the network capacity or an increase in the number of encoding nodes.

Network coding is a new data transmission method which is different from the traditional storage and forwarding routing technology. By using network coding, it can improve the throughput of the media multicast network. This paper study the layer rate allocation optimization problem based on network coding for media multicast network. By optimal allocating layer rate, the total rate of all receiving nodes could be maximized. Because the problem is a nonlinear integer programming problem, so we put forward a heuristic layer rate allocation algorithm. This algorithm decomposes the network graph into child graph by the number of layer according to each receiving node's receiving bandwidth. Thus the minimum value of maximum flow of each receiving node in the child graph is the corresponding layer rate. The results of simulation show that the algorithm can improve network throughput, and the average bandwidth utilization ratio of the receiving node.

Radio resource allocation strategy is one of the key issues in wireless CDMA cellular networks, which not only has to ensure that the system guarantees potentially different QoS requirements from diverse applications, but also fully utilizes the scarce wireless resources. Meanwhile, in CDMA cellular networks, successive interference cancellation (SIC) technique can be employed to reduce multiple access interference and improve system capacity. The possible implementation of radio resource allocation strategies in CDMA cellular networks with SIC, such as Rate Allocation (RA) algorithm, has not yet been extensively investigated. In this paper, we address the issue of the Rate Allocation (RA) algorithm in a DS-CDMA system with the constraint of the received power on the base station. As the problem of rate allocation to maximize the total utilities is a NP problem in the system with variable rate services, we develop two suboptimal Greedy Rate Allocation algorithms, namely upward greedy algorithm (UPGA) and downward greedy algorithm (DNGR). These two greedy algorithms have very low computational complexities. Extensive numerical results show that the proposed RA algorithms have better system performance than other ones with other decoding orders.

In communications networks, the capacity region of multisource network coding is given in terms of the set of entropy functions Gamma*. More broadly, determination of Gamma* would have an impact on converse theorems for multi-terminal problems in information theory. This paper provides several new dualities between entropy functions and network codes. Given a function g ges 0 defined on all subsets of N random variables, we provide a construction for a network multicast problem which is ldquosolvablerdquo if and only if g is the entropy function of a set of quasi-uniform random variables. The underlying network topology is fixed and the multicast problem depends on g only through link capacities and source rates. A corresponding duality is developed for linear network codes, where the constructed multicast problem is linearly solvable if and only if g is linear group characterizable. Relaxing the requirement that the domain of g be subsets of random variables, we obtain a similar duality between polymatroids and the linear programming bound. These duality results provide an alternative proof of the insufficiency of linear (and abelian) network codes, and demonstrate the utility of non-Shannon inequalities to tighten outer bounds on network coding capacity regions.

We study the optimality of separate network-channel coding (SJNC) for three independent sources on the orthogonal access scheme. By SJNC, we mean that the network coded messages are formed from the binary XOR of the sources' messages, followed by channel encoding which is independent of the network coding. Decoding is performed jointly across the network and channel codes. Using standard random coding and joint typical set decoding, we obtain an achievable rate region for SJNC. By allowing time-sharing of codes, the rate region coincides with the capacity region of joint network-channel coding, where network and channel coding are performed jointly. This proves, surprising, that there is no loss of optimality for SJNC.

Physical-layer network coding (PNC) promises a significant gain In overall network throughput for multi-user cooperative communications. In a multi-unicast scenario, one drawback associated with PNC is an additional noise term, coined as network coding (NC) noise, which severely degrades the system data rate. In this paper, our contribution to address this challenging problem is source and relay power control with the objective of maximization of the minimum average achievable rate among all the source-destination pairs subject to a given total power constraint. We further develop a joint power allocation and relay selection scheme, which only rely on long-term channel statistics, to extend our results to general network topologies. We show that the joint optimization problem can be divided into two problems: optimal power allocation and optimal relay selection where the former is scalable and leads to a power assignment algorithm that exhibits the same optimization complexity for any number of sources in the network and the latter can be performed in a decentralized manner. By means of simulations, we validate our theoretical developments and verify the efficiency of our algorithm in improving the average achievable rate compared to a multi-unicast system with no power control or relay selection. We conclude that the proposed algorithm largely combats the adverse effects of NC noise while achieving near optimal fairness.

We consider the power and rate allocation for a transmission in a wireless network where the transmitter knows the channel state of the desired receiver but only knows the statistics of the interferes. While this scenario is often encountered in practice, and extremely important in dense networks, its optimal solution was not yet known. The analysis considers nodes that are distributed according to a homogeneous Poisson point process, and hence applies both to cellular and to ad‐hoc networks. We derive (for the first time) the optimal power allocation at a node, based only on the channel gain to its desired receiver. We also derive the optimal rate allocation in networks that are characterized by the outage rate model. Considering sub‐optimal allocations, we advocate the use of the simple threshold scheduling scheme for power allocation and linear rate adaptation for rate allocation. We prove that these suboptimal schemes are close to optimal, for any channel fading. We also present simulation results over Rayleigh fading channels that show a maximum loss of 1.5% between the advocated schemes and the optimal schemes.

Traditional approach for data transmission in wireless networks with the shared medium, e.g., 802.11 standard family, incurs significant overheads to transmit messages in multiple source - multiple destination (MSMD) schemes. Network coding is new paradigm that increases effectiveness of transmissions in a wireless network with the shared medium and MSMD. We have considered the results of the emulation with COPE-like network coding and various Transmission Control Protocol (TCP) congestion control algorithms in wireless networks. In emulated scheme two nodes stream data to each other through bottleneck relay node. BBR TCP protocol showed the best results in this relaying scheme with enabled COPE-like network coding.

We propose an efficient Adaptive Random Convolutional Network Coding (ARCNC)\nalgorithm to address the issue of field size in random network coding. ARCNC\noperates as a convolutional code, with the coefficients of local encoding\nkernels chosen randomly over a small finite field. The lengths of local\nencoding kernels increase with time until the global encoding kernel matrices\nat related sink nodes all have full rank. Instead of estimating the necessary\nfield size a priori, ARCNC operates in a small finite field. It adapts to\nunknown network topologies without prior knowledge, by locally incrementing the\ndimensionality of the convolutional code. Because convolutional codes of\ndifferent constraint lengths can coexist in different portions of the network,\nreductions in decoding delay and memory overheads can be achieved with ARCNC.\nWe show through analysis that this method performs no worse than random linear\nnetwork codes in general networks, and can provide significant gains in terms\nof average decoding delay in combination networks.\n