Design and Analysis of LT Codes with Decreasing Ripple Size

In this paper we propose a new design of LT codes, which decreases the amount of necessary overhead in comparison to existing designs. The design focuses on a parameter of the LT decoding process called the ripple size. This parameter was also a key element in the design proposed in the original work by Luby. Specifically, Luby argued that an LT code should provide a constant ripple size during decoding. In this work we show that the ripple size should decrease during decoding, in order to reduce the necessary overhead. Initially we motivate this claim by analytical results related to the redundancy within an LT code. We then propose a new design procedure, which can provide any desired achievable decreasing ripple size. The new design procedure is evaluated and compared to the current state of the art through simulations. This reveals a significant increase in performance with respect to both average overhead and error probability at any fixed overhead

Robust live unicast video streaming with rateless codes

"This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.""©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE."We consider live unicast video streaming over a packet erasure channel. To protect the transmitted data, previous solutions use forward error correction (FEC), where the channel code rate is fixed in advance according to an estimation of the packet loss rate. However, these solutions are inefficient under dynamic and unpredictable channel conditions because of the mismatch between the estimated packet loss rate and the actual one.We introduce a new approach based on rateless codes and receiver feedback. For every source block, the sender keeps on transmitting the encoded symbols until it receives an acknowledgment from the receiver indicating that the block was decoded successfully. Within this framework, we provide an efficient algorithm to minimize bandwidth usage while ensuring successful decoding subject to an upper bound on the packet loss rate. Experimental results showed that compared to traditional fixed-rate FEC, our scheme provides significant bandwidth savings for the same playback qualityThis work was supported by the DFG Research Training Group GK-1042

Distributed rate-distortion optimization for rateless coded scalable video

ABSTRACT Recent advances in forward error correction and scalable video coding enable new approaches for robust, distributed streaming in Mobile Ad Hoc Networks (MANETs). This work presents an approach for distribution of real time video by different uncoordinated peerto-peer relay or source nodes in an overlay network on top of a MANET. The approach proposed here allows for distributed, ratedistortion optimized transmission-rate allocation for competing scalable video streams at relay nodes in the overlay network. Furthermore the approach has the desirable feature of path/source diversity for enhancing reliability in connectivity to serving nodes. Signaling overhead within the overlay network is kept at a minimum, since optimizations are done at relay nodes and clients rather than at servers

Adaptive and robust media streaming over multiple channels with bursty losses

This paper addresses the problem of efficiently delivering a layered media stream from multiple senders to a single receiver, over channels that present correlated packet loss patterns. Using a digital fountain approach, the performance of a distributed streaming system is driven by the probability of receiving a given number of packets on aggregate over the multiple channels. In addition, such a system allows to avoid the need for communication between streaming servers. We devise an optimization problem whose solution provides the optimal number of packets that need to be transmitted per channel, in order to maximize the probability of correct decoding for a given media stream. Our findings indicate that it is in general important to consider both the Packet Loss Ratio (PLR) and Average Burst Length (ABL) in channel selection problems such as multipath routing or rate aggregation on multiple bursty channels. Finally we present a low-complexity algorithm which is able to quickly find a suboptimal yet effective solution to the combinatorial optimization problem

Adaptive Prioritized Random Linear Coding and Scheduling for Layered Data Delivery From Multiple Servers

In this paper, we deal with the problem of jointly determining the optimal coding strategy and the scheduling decisions when receivers obtain layered data from multiple servers. The layered data is encoded by means of prioritized random linear coding (PRLC) in order to be resilient to channel loss while respecting the unequal levels of importance in the data, and data blocks are transmitted simultaneously in order to reduce decoding delays and improve the delivery performance. We formulate the optimal coding and scheduling decisions problem in our novel framework with the help of Markov decision processes (MDP), which are effective tools for modeling adapting streaming systems. Reinforcement learning approaches are then proposed to derive reduced computational complexity solutions to the adaptive coding and scheduling problems. The novel reinforcement learning approaches and the MDP solution are examined in an illustrative example for scalable video transmission . Our methods offer large performance gains over competing methods that deliver the data blocks sequentially. The experimental evaluation also shows that our novel algorithms offer continuous playback and guarantee small quality variations which is not the case for baseline solutions. Finally, our work highlights the advantages of reinforcement learning algorithms to forecast the temporal evolution of data demands and to decide the optimal coding and scheduling decisions

Resource allocation and adaptive scheduling for scalable video streaming

The obvious recent advances in areas such as video compression and network architectures allow for the deployment of novel video distribution applications. These have the potential to provide ubiquitous media access to end users. In recent years, applications based on audio and video streaming have turned out to be immensely popular and the Internet has become the most widely used vector for media content distribution, due to its high availability and connectivity. However, the nature of the Internet infrastructure is not adapted to the specific characteristics of multimedia traffic, which presents a certain tolerance to losses, but strict delay and high bandwidth requirements. In this thesis, our goal is to improve the efficiency of media delivery over the existing network architecture. In order to do so we consider the delivery of scalable video in three main delivery scenarios, namely one-to-one client server architectures, one-to-many broadcasting architectures, and many-to-one distributed streaming architectures. First, we propose a distributed media-friendly rate allocation algorithm for the delivery of both finely and coarsely scalable video streams. Unlike existing solutions, our algorithm explicitly takes the characteristics of media streams into consideration. As a result, it provides rate allocations that better fit the heterogeneous characteristics of media streams. We outline an implementation that is robust to random feedback delays and that permits a scalable deployment of the algorithm. The rate allocation that is computed by our algorithm achieves network stability and high bandwidth utilization. It moreover allows to maximize the average received quality for all streams that are delivered in the network. While considering the transmission of coarsely layered streams, we derive conditions on the encoding rates of the video layers. These conditions depend on the allowed end-to-end delay and on the rate allocation algorithm that controls the sending rates. They allow us to take full advantage of the allocated transmission rates. Second, we investigate the problem of jointly addressing the needs of multiple receivers that consume different versions of a layered media stream in a broadcasting scenario. We provide optimal scheduling algorithms that jointly optimize the playback delay and the buffer occupancy at all of these receivers when the used channel is known. Furthermore we analyze low complexity heuristics based optimization techniques, which provide close to optimal results when only limited channel knowledge is available. Finally, we explore the possibility to exploit the inherent network diversity that is provided by the Internet infrastructure. In particular, we consider media delivery schemes where multiple senders are available for the transmission of a scalable video stream to a single client. Such an architecture is referred to as a distributed streaming architecture. It has the benefit of aggregating multiple unreliable channels into a single more robust channel with high availability. Through the use of Fountain codes, we are able to transform the distributed streaming problem into a rate allocation problem of lower complexity. The solution to this problem is shown to depend not only on the average packet loss rate, but also on the average length of packet loss bursts that are observed on each of the available channels. The coding scheme that we suggest enables our system to adapt the streamed content to the network characteristics, as well as to the needs of the receiving client