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 .
Application-layer forward error correction (FEC) is used in many multimedia communication systems to address the problem of packet loss in lossy packet networks. One powerful form of application-layer FEC is unequal error protection which protects the information symbols according to their importance. We propose a method for unequal error protection with a Fountain code. When the information symbols were partitioned into two protection classes (most important and least important), our method required a smaller transmission bit budget to achieve low bit error rates compared to the two state-of-the-art techniques. We also compared our method to the two state-of-the-art techniques for video unicast and multicast over a lossy network. Simulations for the scalable video coding (SVC) extension of the H.264 AVC standard showed that our method required a smaller transmission bit budget to achieve high-quality video. This paper presents a framework for efficiently streaming scalable video from multiple servers over heterogeneous network paths. We propose to use rateless codes, or Fountain codes, such that each server acts as an independent source, without the need to coordinate its sending strategy with other servers. In this case, the problem of maximizing the received video quality and minimizing the bandwidth usage, is simply reduced to a rate allocation problem. We provide an optimal solution for an ideal scenario where the loss probvability on each server-client path is exactly known. We then present a heuristic-based algorithm, which implements an unequal error protection scheme for the more realistic case of imperfect knowledge of the loss probabilities. Simulation results finally demonstrate the efficiency of the proposed algorithm, in distributed streaming scenarios over lossy channels. Digital fountain codes are becoming increasingly important for multimedia communications over networks subject to packet erasures. These codes have significantly lower complexity than Reed-Solomon ones, exhibit high erasure correction performance, and are very well suited to generating multiple equally important descriptions of a source. In this paper we propose an innovative scheme for streaming multimedia contents by using digital fountain codes applied over sliding windows, along with a suitably modified belief-propagation decoder. The use of overlapped windows allows one to have a virtually extended block, which yields superior performance in terms of packet recovery. Simulation results using LT codes show that the proposed algorithm has better performance in terms of efficiency, reliability and memory with respect to fixed-window encoding. Fountain codes were introduced as an efficient and universal forward error correction (FEC) solution for data multicast over lossy packet networks. They have recently been proposed for large scale multimedia content delivery in practical multimedia distribution systems. However, standard fountain codes, such as LT or Raptor codes, are not designed to meet unequal error protection (UEP) requirements typical in real-time scalable video multicast applications. In this paper, we propose recently introduced UEP expanding window fountain (EWF) codes as a flexible and efficient solution for real-time scalable video multicast. We demonstrate that the design flexibility and UEP performance make EWF codes ideally suited for this scenario, i.e., EWF codes offer a number of design parameters to be ldquotunedrdquo at the server side to meet the different reception criteria of heterogeneous receivers. The performance analysis using both analytical results and simulation experiments of H.264 scalable video coding (SVC) multicast to heterogeneous receiver classes confirms the flexibility and efficiency of the proposed EWF-based FEC solution. Digital fountain codes have emerged as a low-complexity alternative to Reed-Solomon codes for erasure correction. The applications of these codes are relevant especially in the field of wireless video, where low encoding and decoding complexity is crucial. In this paper, we introduce a new class of digital fountain codes based on a sliding-window approach applied to Raptor codes. These codes have several properties useful for video applications, and provide better performance than classical digital fountains. Then, we propose an application of sliding-window Raptor codes to wireless video broadcasting using scalable video coding. The rates of the base and enhancement layers, as well as the number of coded packets generated for each layer, are optimized so as to yield the best possible expected quality at the receiver side, and providing unequal loss protection to the different layers according to their importance. The proposed system has been validated in a UMTS broadcast scenario, showing that it improves the end-to-end quality, and is robust towards fluctuations in the packet loss rate. Recent advances in forward error correction and scalable video coding enable new approaches for robust, distributed streaming in Mobile Ad Hoc Networks (MANETs). This paper presents an approach for distribution of real time video by uncoordinated peer-to-peer relay or source nodes in an overlay network on top of a MANET. The approach proposed here allows for distributed, rate-distortion optimized transmission-rate allocation for competing scalable video streams at relay nodes in the overlay network. The approach has the desirable feature of path source diversity that can be used for enhancing reliability in connectivity to serving nodes and or attaining a higher throughput. The distributed approach reduces signaling overhead as well as avoiding scalability issues that come with centralized processing in MANETs. Results show a significant performance gain over both single-server systems and previously proposed multi-source systems.