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.

Adaptive media streaming over multipath networks

Dan Jurca

With the latest developments in video coding technology and fast deployment of end-user broadband internet connections, real-time media applications become increasingly interesting for both private users and businesses. However, the internet remains a best-effort service network unable to guarantee the stringent requirements of the media application, in terms of high, constant bandwidth, low packet loss rate and transmission delay. Therefore, efficient adaptation mechanisms must be derived in order to bridge the application requirements with the transport medium characteristics. Lately, different network architectures, e.g., peer-to-peer networks, content distribution networks, parallel wireless services, emerge as potential solutions for reducing the cost of communication or infrastructure, and possibly improve the application performance. In this thesis, we start from the path diversity characteristic of these architectures, in order to build a new framework, specific for media streaming in multipath networks. Within this framework we address important issues related to an efficient streaming process, namely path selection and rate allocation, forward error correction and packet scheduling over multiple transmission paths. First we consider a network graph between the streaming server and the client, offering multiple possible transmission paths to the media application. We are interested in finding the optimal subset of paths employed for data transmission, and the optimal rate allocation on these paths, in order to optimize a video distortion metric. Our in-depth analysis of the proposed scenario eventually leads to the derivation of three important theorems, which, in turn represent the basis for an optimal, linear time algorithm that finds the solution to our optimization problem. At the same time, we provide distributed protocols which compute the optimal solution in a distributed way, suitable for large scale network graphs, where a centralized solution is too expensive. Next, we address the problem of forward error correction for scalable media streaming over multiple network paths. We propose various algorithms for error protection in a multipath scenario, and we assess the opportunity of in-network error correction. Our analysis stresses the advantage of being flexible in the scheduling and error correction process on multiple network paths, and emphasizes the limitations of possible real systems implementations, where application choices are limited. Finally, we observe the improvements brought by in-network processing of transmitted media flows, in the case of heterogeneous networks, when link parameters vary greatly. Once the rate allocation and error correction issues are addressed, we discuss the packet scheduling problem over multiple transmission paths. We rely on a scalable bitstream packet model inspired from the media coding process, where media packets have different priorities and dependencies. Based on the concept of data pre-fetch, and on a strict time analysis of the transmission process, we propose fast algorithms for efficient packet scheduling over multiple paths. We ensure media graceful degradation at the client in adverse network conditions by careful load balancing among transmission paths, and by conservative scheduling which transparently absorb undetected network variations, or network estimation errors. The final part of this thesis presents a possible system for media streaming where our proposed mechanisms and protocols can be straightforwardly implemented. We describe a wireless setup where clients can access various applications over possibly multiple wireless services. In this setup, we solve the rate allocation problem with the final goal of maximizing the overall system performance. To this end, we propose a unifying quality metric which maps the individual performance of each application (including streaming) to a common value, later used in the optimization process. We propose a fast algorithm for computing a close to optimal solution to this problem and we show that compared to other traditional methods, we achieve a more fair performance, better adaptable to changing network environments.

EPFL2007

Signaling and Reciprocity

Wojciech Galuba

Complex networks exist for a number of purposes. The neural, metabolic and food networks ensure our survival, while the social, economic, transportation and communication networks allow us to prosper. Independently of the purposes and particularities of the physical embodiment of the networks, one of their fundamental functions is the delivery of information from one part of the network to another. Gossip and diseases diffuse in the social networks, electrochemical signals propagate in the neural networks and data packets travel in the Internet. Engineering networks for robust information flows is a challenging task. First, the mechanism through which the network forms and changes its topology needs to be defined. Second, within a given topology, the information must be routed to the appropriate recipients. Third, both the network formation and the routing mechanisms need to be robust against a wide spectrum of failures and adversaries. Fourth, the network formation, routing and failure recovery must operate under the resource constraints, either intrinsic or extrinsic to the network. Finally, the autonomously operating parts of the network must be incentivized to contribute their resources to facilitate the information flows. This thesis tackles the above challenges within the context of several types of networks: 1) peer-to-peer overlays – computers interconnected over the Internet to form an overlay in which participants provide various services to one another, 2) mobile ad-hoc networks – mobile nodes distributed in physical space communicating wirelessly with the goal of delivering data from one part of the network to another, 3) file-sharing networks – networks whose participants interconnect over the Internet to exchange files, 4) social networks – humans disseminating and consuming information through the network of social relationships. The thesis makes several contributions. Firstly, we propose a general algorithm, which given a set of nodes embedded in an arbitrary metric space, interconnects them into a network that efficiently routes information. We apply the algorithm to the peer-to-peer overlays and experimentally demonstrate its high performance, scalability as well as resilience to continuous peer arrivals and departures. We then shift our focus to the problem of the reliability of routing in the peer-to-peer overlays. Each overlay peer has limited resources and when they are exhausted this ultimately leads to delayed or lost overlay messages. All the solutions addressing this problem rely on message redundancy, which significantly increases the resource costs of fault-tolerance. We propose a bandwidth-efficient single-path Forward Feedback Protocol (FFP) for overlay message routing in which successfully delivered messages are followed by a feedback signal to reinforce the routing paths. Internet testbed evaluation shows that FFP uses 2-5 times less network bandwidth than the existing protocols relying on message redundancy, while achieving comparable fault-tolerance levels under a variety of failure scenarios. While the Forward Feedback Protocol is robust to message loss and delays, it is vulnerable to malicious message injection. We address this and other security problems by proposing Castor, a variant of FFP for mobile ad-hoc networks (MANETs). In Castor, we use the same general mechanism as in FFP; each time a message is routed, the routing path is either enforced or weakened by the feedback signal depending on whether the routing succeeded or not. However, unlike FFP, Castor employs cryptographic mechanisms for ensuring the integrity and authenticity of the messages. We compare Castor to four other MANET routing protocols. Despite Castor's simplicity, it achieves up to 40% higher packet delivery rates than the other protocols and recovers at least twice as fast as the other protocols in a wide range of attacks and failure scenarios. Both of our protocols, FFP and Castor, rely on simple signaling to improve the routing robustness in peer-to-peer and mobile ad-hoc networks. Given the success of the signaling mechanism in shaping the information flows in these two types of networks, we examine if signaling plays a similar crucial role in the on-line social networks. We characterize the propagation of URLs in the social network of Twitter. The data analysis uncovers several statistical regularities in the user activity, the social graph, the structure of the URL cascades as well as the communication and signaling dynamics. Based on these results, we propose a propagation model that accurately predicts which users are likely to mention which URLs. We outline a number of applications where the social network information flow modelling would be crucial: content ranking and filtering, viral marketing and spam detection. Finally, we consider the problem of freeriding in peer-to-peer file-sharing applications, when users can download data from others, but never reciprocate by uploading. To address the problem, we propose a variant of the BitTorrent system in which two peers are only allowed to connect if their owners know one another in the real world. When the users know which other users their BitTorrent client connects to, they are more likely to cooperate. The social network becomes the content distribution network and the freeriding problem is solved by leveraging the social norms and reciprocity to stabilize cooperation rather than relying on technological means. Our extensive simulation shows that the social network topology is an efficient and scalable content distribution medium, while at the same time provides robustness to freeriding.

EPFL2011

Streaming of Scalable Video from Multiple Servers using Rateless Codes

Pascal Frossard, Jean-Paul Wagner

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 probability 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 effi- ciency of the proposed algorithm, in distributed streaming scenarios over lossy channels.