Network coding permits to deploy distributed packet delivery algorithms that locally adapt to the network availability in media streaming applications. However, it may also increase delay and computational complexity if it is not implemented efficiently. We address here the effective placement of a limited number of nodes that implement randomized network coding in overlay networks, so that the goodput is kept high while the delay for decoding stays small in streaming applications. We first estimate the decoding delay at each client, which depends on the innovative rate in the network. This estimation permits to identify the nodes that have to perform coding in order to reduce the decoding delay. We then propose two iterative algorithms for selecting the nodes that should perform network coding. The first algorithm relies on the knowledge of the full network statistics. The second algorithm uses only local network statistics at each node. Simulation results show that large performance gains can be achieved with the selection of only a few network coding nodes. Moreover, the second algorithm performs very closely to the central estimation strategy, which demonstrates that the network coding nodes can be selected efficiently with help of a distributed innovative flow rate estimation solution. Our solution provides large gains in terms of throughput, delay, and video quality in realistic overlay networks when compared to methods that employ traditional streaming strategies as well as random network coding nodes selection algorithms.
In general, the previous works about the selection of coding nodes do not consider delay issues, which are most important in streaming applications. The problem of the selection of network processing nodes in multimedia streaming applications has been addressed in @cite_15 in a framework that is however slightly different than ours. The placement of a limited number of network-embedded FEC nodes (NEF) is considered in networks that are organized into multicast trees. The placement is chosen in order to enhance the robustness to transmission errors and to improve the network's throughput. NEF nodes first decode and successively re-encode the recovered packets in order to increase the symbol diversity. A greedy algorithm is proposed for placing NEF nodes. Although the proposed method is efficient, it is computationally expensive and unrealistic to be deployed in dynamic networks. In contrast to @cite_15 , we consider the placement of processing nodes in the more general case of overlay mesh networks with randomized network coding for distributed packet delivery.
@cite_15
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Forward error correction (FEC) schemes have been proposed and used successfully for multicasting realtime video content to groups of users. Under traditional IP multicast, application-level FEC can only be implemented on an end-to-end basis between the sender and the clients. Emerging overlay and peer-to-peer (p2p) networks open the door for new paradigms of network FEC. The deployment of FEC within these emerging networks has received very little attention (if any). In this paper, we analyze and optimize the impact of network-embedded FEC (NEF) in overlay and p2p multimedia multicast networks. Under NEF, we place FEC codecs in selected intermediate nodes of a multicast tree. The NEF codecs detect and recover lost packets within FEC blocks at earlier stages before these blocks arrive at deeper intermediate nodes or at the final leaf nodes. This approach significantly reduces the probability of receiving undecodable FEC blocks. In essence, the proposed NEF codecs work as signal regenerators in a communication system and can reconstruct most of the lost data packets without requiring retransmission. We develop an optimization algorithm for the placement of NEF codecs within random multicast trees. Based on extensive H.264 video simulations, we show that this approach provides significant improvements in video quality, both visually and in terms of PSNR values. We address the problem of prioritized video streaming over lossy overlay networks. We propose to exploit network path diversity via a novel randomized network coding (RNC) approach that provides unequal error protection (UEP) to the packets conveying the video content. We design a distributed receiver-driven streaming solution, where a client requests packets from the different priority classes from its neighbors in the overlay. Based on the received requests, a node in turn forwards combinations of the selected packets to the requesting peers. Choosing a network coding strategy at every node can be cast as an optimization problem that determines the rate allocation between the different packet classes such that the average distortion at the requesting peer is minimized. As the optimization problem has log-concavity properties, it can be solved with low complexity by an iterative algorithm. Our simulation results demonstrate that the proposed scheme respects the relative priorities of the different packet classes and achieves a graceful quality adaptation to network resource constraints. Therefore, our scheme substantially outperforms reference schemes such as baseline network coding techniques as well as solutions that employ rateless codes with built-in UEP properties. The performance evaluation provides additional evidence of the substantial robustness of the proposed scheme in a variety of transmission scenarios. We consider the canonical shared link caching network formed by a source node, hosting a library of @math information messages (files), connected via a noiseless multicast link to @math user nodes, each equipped with a cache of size @math files. Users request files independently at random according to an a-priori known demand distribution q. A coding scheme for this network consists of two phases: cache placement and delivery. The cache placement is a mapping of the library files onto the user caches that can be optimized as a function of the demand statistics, but is agnostic of the actual demand realization. After the user demands are revealed, during the delivery phase the source sends a codeword (function of the library files, cache placement, and demands) to the users, such that each user retrieves its requested file with arbitrarily high probability. The goal is to minimize the average transmission length of the delivery phase, referred to as rate (expressed in channel symbols per file). In the case of deterministic demands, the optimal min-max rate has been characterized within a constant multiplicative factor, independent of the network parameters. The case of random demands was previously addressed by applying the order-optimal min-max scheme separately within groups of files requested with similar probability. However, no complete characterization of order-optimality was previously provided for random demands under the average rate performance criterion. In this paper, we consider the random demand setting and, for the special yet relevant case of a Zipf demand distribution, we provide a comprehensive characterization of the order-optimal rate for all regimes of the system parameters, as well as an explicit placement and delivery scheme achieving order-optimal rates. We present also numerical results that confirm the superiority of our scheme with respect to previously proposed schemes for the same setting. Flooding based querying and broadcasting schemes have low hop-delays of Theta(1 R(n)) to reach any node that is a unit distance away, where R(n) is the transmission range of any sensor node. However, in sensor networks with large radio ranges, flooding based broadcasting schemes cause many redundant transmissions leading to a broadcast storm problem. In this paper, we study the role of geographic information and state information (i.e., memory of previous messages or transmissions) in reducing the redundant transmissions in the network. We consider three broadcasting schemes with varying levels of local information where nodes have: (i) no geographic or state information, (ii) coarse geographic information about the origin of the broadcast, and (Hi) no geographic information, but remember previously received messages. For each of these network models, we demonstrate localized forwarding algorithms for broadcast (based on geography or state information) that achieve significant reductions in the transmission overheads while maintaining hop-delays comparable to flooding based schemes. We also consider the related problem of broadcasting to a set of "spatially uniform" points in the network (lattice points) in the regime where all nodes have only a local sense of direction and demonstrate an efficient "sparse broadcast" scheme based on a branching random walk that has a low number of packet transmissions. Thus, our results show that even with very little local information, it is possible to make broadcast schemes significantly more efficient.