Source-Channel Diversity for Parallel Channels

We consider transmitting a source across a pair of independent, non-ergodic channels with random states (e.g., slow fading channels) so as to minimize the average distortion. The general problem is unsolved. Hence, we focus on comparing two commonly used source and channel encoding systems which correspond to exploiting diversity either at the physical layer through parallel channel coding or at the application layer through multiple description source coding. For on-off channel models, source coding diversity offers better performance. For channels with a continuous range of reception quality, we show the reverse is true. Specifically, we introduce a new figure of merit called the distortion exponent which measures how fast the average distortion decays with SNR. For continuous-state models such as additive white Gaussian noise channels with multiplicative Rayleigh fading, optimal channel coding diversity at the physical layer is more efficient than source coding diversity at the application layer in that the former achieves a better distortion exponent. Finally, we consider a third decoding architecture: multiple description encoding with a joint source-channel decoding. We show that this architecture achieves the same distortion exponent as systems with optimal channel coding diversity for continuous-state channels, and maintains the the advantages of multiple description systems for on-off channels. Thus, the multiple description system with joint decoding achieves the best performance, from among the three architectures considered, on both continuous-state and on-off channels.Comment: 48 pages, 14 figure

A Redundant Traffic Load Routing Policy in Complex Network

Abstract: To tackle the network congestion issue caused by redundant traffic, a redundant traffic load routing policy is proposed. During the network dynamics, nodes can be aware of the traffic redundancy and take it into consideration to decide paths choosing. Nodes in the network transfer packets by trading off the shortest path length and the edges' dynamic information flow in order to maximize network communication capacity. The processing ability of a node is proportional to its weighted value, which is defined as its nodes' degree. Theoretical analysis shows that network phase transition point is the hybrid effects of network edges' bandwidth and nodes' processing capability. Simulation is carried out in BBV weighted network and results show that this novel routing policy can avoid choosing nodes which are more vulnerable to congestion. It can alleviate nodes congestion and improve the network overall throughput

Meta-heuristic algorithms for optimized network flow wavelet-based image coding

Optimal multipath selection to maximize the received multiple description coding (MDCs) in a lossy network model is proposed. Multiple description scalar quantization (MDSQ) has been applied to the wavelet coefficients of a color image to generate the MDCs which are combating transmission loss over lossy networks. In the networks, each received description raises the reconstruction quality of an MDC-coded signal (image, audio or video). In terms of maximizing the received descriptions, a greater number of optimal routings between source and destination must be obtained. The rainbow network flow (RNF) collaborated with effective meta-heuristic algorithms is a good approach to resolve it. Two meta-heuristic algorithms which are genetic algorithm (GA) and particle swarm optimization (PSO) have been utilized to solve the multi-objective optimization routing problem for finding optimal routings each of which is assigned as a distinct color by RNF to maximize the coded descriptions in a network model. By employing a local search based priority encoding method, each individual in GA and particle in PSO is represented as a potential solution. The proposed algorithms are compared with the multipath Dijkstra algorithm (MDA) for both finding optimal paths and providing reliable multimedia communication. The simulations run over various random network topologies and the results show that the PSO algorithm finds optimal routings effectively and maximizes the received MDCs with assistance of RNF, leading to reduce packet loss and increase throughput

Network Resource Allocation for Competing Multiple Description Transmissions

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