Inter-session Network Coding for Transmitting Multiple Layered Streams over Single-hop Wireless Networks

This paper studies the problem of transmitting multiple independent layered video streams over single-hop wireless networks using network coding (NC). We combine feedback-free random linear NC (RLNC) with unequal error protection (UEP) and our goal is to investigate the benefits of coding across streams, i.e. inter session NC. To this end, we present a transmission scheme that in addition to mixing packets of different layers of each stream (intra-session NC), mixes packets of different streams as well. Then, we propose the analytical formulation of the layer decoding probabilities for each user and utilize it to define a theoretical performance metric. Assessing this performance metric under various scenarios, it is observed that inter-session NC improves the trade-off among the performances of users. Furthermore, the analytical results show that the throughput gain of inter-session NC over intra-session NC increases with the number of independent streams and also by increasing packet error rate, but degrades as network becomes more heterogeneous.Comment: Accepted to be presented at 2014 IEEE Information Theory Workshop (ITW), 5 pages, 4 figure

Random Linear Network Coding for Wireless Layered Video Broadcast: General Design Methods for Adaptive Feedback-free Transmission

This paper studies the problem of broadcasting layered video streams over heterogeneous single-hop wireless networks using feedback-free random linear network coding (RLNC). We combine RLNC with unequal error protection (UEP) and our main purpose is twofold. First, to systematically investigate the benefits of UEP+RLNC layered approach in servicing users with different reception capabilities. Second, to study the effect of not using feedback, by comparing feedback-free schemes with idealistic full-feedback schemes. To these ends, we study `expected percentage of decoded frames' as a key content-independent performance metric and propose a general framework for calculation of this metric, which can highlight the effect of key system, video and channel parameters. We study the effect of number of layers and propose a scheme that selects the optimum number of layers adaptively to achieve the highest performance. Assessing the proposed schemes with real H.264 test streams, the trade-offs among the users' performances are discussed and the gain of adaptive selection of number of layers to improve the trade-offs is shown. Furthermore, it is observed that the performance gap between the proposed feedback-free scheme and the idealistic scheme is very small and the adaptive selection of number of video layers further closes the gap.Comment: 15 pages, 12 figures, 3 tables, Under 2nd round of review, IEEE Transactions on Communication

Small-Sample Inferred Adaptive Recoding for Batched Network Coding

Batched network coding is a low-complexity network coding solution to feedbackless multi-hop wireless packet network transmission with packet loss. The data to be transmitted is encoded into batches where each of which consists of a few coded packets. Unlike the traditional forwarding strategy, the intermediate network nodes have to perform recoding, which generates recoded packets by network coding operations restricted within the same batch. Adaptive recoding is a technique to adapt the fluctuation of packet loss by optimizing the number of recoded packets per batch to enhance the throughput. The input rank distribution, which is a piece of information regarding the batches arriving at the node, is required to apply adaptive recoding. However, this distribution is not known in advance in practice as the incoming link's channel condition may change from time to time. On the other hand, to fully utilize the potential of adaptive recoding, we need to have a good estimation of this distribution. In other words, we need to guess this distribution from a few samples so that we can apply adaptive recoding as soon as possible. In this paper, we propose a distributionally robust optimization for adaptive recoding with a small-sample inferred prediction of the input rank distribution. We develop an algorithm to efficiently solve this optimization with the support of theoretical guarantees that our optimization's performance would constitute as a confidence lower bound of the optimal throughput with high probability.Comment: 7 pages, 2 figures, accepted in ISIT-21, appendix adde

Scalable Video Streaming with Prioritised Network Coding on End-System Overlays

PhDDistribution over the internet is destined to become a standard approach for live broadcasting of TV or events of nation-wide interest. The demand for high-quality live video with personal requirements is destined to grow exponentially over the next few years. Endsystem multicast is a desirable option for relieving the content server from bandwidth bottlenecks and computational load by allowing decentralised allocation of resources to the users and distributed service management. Network coding provides innovative solutions for a multitude of issues related to multi-user content distribution, such as the coupon-collection problem, allocation and scheduling procedure. This thesis tackles the problem of streaming scalable video on end-system multicast overlays with prioritised push-based streaming. We analyse the characteristic arising from a random coding process as a linear channel operator, and present a novel error detection and correction system for error-resilient decoding, providing one of the first practical frameworks for Joint Source-Channel-Network coding. Our system outperforms both network error correction and traditional FEC coding when performed separately. We then present a content distribution system based on endsystem multicast. Our data exchange protocol makes use of network coding as a way to collaboratively deliver data to several peers. Prioritised streaming is performed by means of hierarchical network coding and a dynamic chunk selection for optimised rate allocation based on goodput statistics at application layer. We prove, by simulated experiments, the efficient allocation of resources for adaptive video delivery. Finally we describe the implementation of our coding system. We highlighting the use rateless coding properties, discuss the application in collaborative and distributed coding systems, and provide an optimised implementation of the decoding algorithm with advanced CPU instructions. We analyse computational load and packet loss protection via lab tests and simulations, complementing the overall analysis of the video streaming system in all its components

Galois : a system for parallel execution of irregular algorithms

textA programming model which allows users to program with high productivity and which produces high performance executions has been a goal for decades. This dissertation makes progress towards this elusive goal by describing the design and implementation of the Galois system, a parallel programming model for shared-memory, multicore machines. Central to the design is the idea that scheduling of a program can be decoupled from the core computational operator and data structures. However, efficient programs often require application-specific scheduling to achieve best performance. To bridge this gap, an extensible and abstract scheduling policy language is proposed, which allows programmers to focus on selecting high-level scheduling policies while delegating the tedious task of implementing the policy to a scheduler synthesizer and runtime system. Implementations of deterministic and prioritized scheduling also are described. An evaluation of a well-studied benchmark suite reveals that factoring programs into operators, schedulers and data structures can produce significant performance improvements over unfactored approaches. Comparison of the Galois system with existing programming models for graph analytics shows significant performance improvements, often orders of magnitude more, due to (1) better support for the restrictive programming models of existing systems and (2) better support for more sophisticated algorithms and scheduling, which cannot be expressed in other systems.Computer Science