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

Control of Multiple Remote Servers for Quality-Fair Delivery of Multimedia Contents

This paper proposes a control scheme for the quality-fair delivery of several encoded video streams to mobile users sharing a common wireless resource. Video quality fairness, as well as similar delivery delays are targeted among streams. The proposed controller is implemented within some aggregator located near the bottleneck of the network. The transmission rate among streams is adapted based on the quality of the already encoded and buffered packets in the aggregator. Encoding rate targets are evaluated by the aggregator and fed back to each remote video server (fully centralized solution), or directly evaluated by each server in a distributed way (partially distributed solution). Each encoding rate target is adjusted for each stream independently based on the corresponding buffer level or buffering delay in the aggregator. Communication delays between the servers and the aggregator are taken into account. The transmission and encoding rate control problems are studied with a control-theoretic perspective. The system is described with a multi-input multi-output model. Proportional Integral (PI) controllers are used to adjust the video quality and control the aggregator buffer levels. The system equilibrium and stability properties are studied. This provides guidelines for choosing the parameters of the PI controllers. Experimental results show the convergence of the proposed control system and demonstrate the improvement in video quality fairness compared to a classical transmission rate fair streaming solution and to a utility max-min fair approach

Distributed Rate Allocation Policies for Multi-Homed Video Streaming over Heterogeneous Access Networks

We consider the problem of rate allocation among multiple simultaneous video streams sharing multiple heterogeneous access networks. We develop and evaluate an analytical framework for optimal rate allocation based on observed available bit rate (ABR) and round-trip time (RTT) over each access network and video distortion-rate (DR) characteristics. The rate allocation is formulated as a convex optimization problem that minimizes the total expected distortion of all video streams. We present a distributed approximation of its solution and compare its performance against H-infinity optimal control and two heuristic schemes based on TCP-style additive-increase-multiplicative decrease (AIMD) principles. The various rate allocation schemes are evaluated in simulations of multiple high-definition (HD) video streams sharing multiple access networks. Our results demonstrate that, in comparison with heuristic AIMD-based schemes, both media-aware allocation and H-infinity optimal control benefit from proactive congestion avoidance and reduce the average packet loss rate from 45% to below 2%. Improvement in average received video quality ranges between 1.5 to 10.7 dB in PSNR for various background traffic loads and video playout deadlines. Media-aware allocation further exploits its knowledge of the video DR characteristics to achieve a more balanced video quality among all streams.Comment: 12 pages, 22 figure

Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs

Bandwidth efficient multi-station wireless streaming based on complete complementary sequences

Data streaming from multiple base stations to a client is recognized as a robust technique for multimedia streaming. However the resulting transmission in parallel over wireless channels poses serious challenges, especially multiple access interference, multipath fading, noise effects and synchronization. Spread spectrum techniques seem the obvious choice to mitigate these effects, but at the cost of increased bandwidth requirements. This paper proposes a solution that exploits complete complementary spectrum spreading and data compression techniques jointly to resolve the communication challenges whilst ensuring efficient use of spectrum and acceptable bit error rate. Our proposed spreading scheme reduces the required transmission bandwidth by exploiting correlation among information present at multiple base stations. Results obtained show 1.75 Mchip/sec (or 25%) reduction in transmission rate, with only up to 6 dB loss in frequency-selective channel compared to a straightforward solution based solely on complete complementary spectrum spreading