Wai-tian Tan

On multiple description streaming with content delivery networks

We propose a system that improves the performance of streaming media CDN by exploiting the path diversity provided by existing CDN infrastructure. Path diversity is provided by the different network paths that exist between a client and its nearby edge servers; and multiple description (MD) coding is coupled with this path diversity to provide resilience to losses. In our system, MD coding is used to code a media stream into multiple complementary descriptions, which are distributed across the edge servers in the CDN. When a client requests a media stream, it is directed to multiple nearby servers which host complementary descriptions. These servers simultaneously stream these complementary descriptions to the client over different network paths. This paper provides distortion models for MDC video and conventional video. We use these models to select the optimal pair of servers with complementary descriptions for each client while accounting for path lengths and path jointness and disjointness. We also use these models to evaluate the performance of MD streaming over CDN in a number of real and generated network topologies. Our results show that distortion reduction by about 20 to 40% can be realized even when the underlying CDN is not designed with MDC streaming in mind. Also, for certain topologies, MDC requires about 50% fewer CDN servers than conventional streaming techniques to achieve the same distortion at the clients.

Modeling path diversity for multiple description video communication

The use of multiple description (MD) video coding and path diversity has been proposed to provide improved performance over lossy packet networks [1]. The goal of this work was to develop models to accurately and quickly predict and compare the distortion of MD video coding and path diversity against conventional single description (SD) video delivered over a single path. In the process, we developed (1) a model for the loss process of a two-path path diversity system, and (2) a distortion model that maps the loss model to MD distortion values. Given these models we present a number of comparisons between MD video coding and path diversity and conventional SD video over a single path. The proposed model for path diversity may also be useful in other applications not related to MD coding. Furthermore, other forms of MD coding may be analyzed using similar models for MD distortion.

Rate-distortion hint tracks for adaptive video streaming

We present a technique for low-complexity rate-distortion (R-D) optimized adaptive video streaming based on the concept of rate-distortion hint track (RDHT). RDHTs store the precomputed characteristics of a compressed media source that are crucial for high performance online streaming but difficult to compute in real time. This enables low-complexity adaptation to variations in transport conditions such as available data rate or packet loss. An RDHT-based streaming system has three components: 1) information that summarizes the R-D attributes of the media; 2) an algorithm for using the RDHT to predict the distortion for a feasible packet schedule; and 3) a method for determining the best packet schedule to adapt the streaming to the communication channel. A family of distortion models, denoted distortion chains, are presented which accurately predict the distortion produced by arbitrary packet loss patterns. Two distortion chain models are examined which lead to two RDHT-based techniques. We evaluate the proposed techniques for two canonical problems in streaming media, adaptation to available data rate and to packet loss. Experimental results demonstrate that for the difficult case of nonscalably coded H.264 video, the proposed systems provide significant performance gains over conventional low-complexity streaming systems, and achieve this gain with a comparable level of complexity making them suitable for online R-D optimized streaming.

Design and implementation of an extrusion-based break-in detector for personal computers

An increasing variety of malware, such as worms, spyware and adware, threatens both personal and business computing. Remotely controlled bot networks of compromised systems are growing quickly. In this paper, we tackle the problem of automated detection of break-ins caused by unknown malware targeting personal computers. We develop a host based system, BINDER (Break-IN DEtectoR), to detect break-ins by capturing user unintended malicious outbound connections (referred to as extrusions). To infer user intent, BINDER correlates outbound connections with user-driven input at the process level under the assumption that user intent is implied by user-driven input. Thus BINDER can detect a large class of unknown malware such as worms, spyware and adware without requiring signatures. We have successfully used BINDER to detect real world spyware on daily used computers and email worms on a controlled testbed with very small false positives

Research and design of a mobile streaming media content delivery network

Delivering media to large numbers of mobile users presents challenges due to the stringent requirements of streaming media, mobility, wireless, and scaling to support large numbers of users. This paper presents a mobile streaming media content delivery network (MSM-CDN) designed to overcome these challenges. The MSM-CDN is a network overlay consisting of overlay servers on top of the existing network; these overlay servers are control points that facilitate end-to-end media delivery and mid-network media services. This paper presents an overview of the MSM-CDN system architecture, and describes the testbed prototype that we built based on these architectural principles. The MSM-CDN provides a new platform for media delivery, and we describe a number of research directions related to the MSM-CDN.

Optimized video streaming for networks with varying delay

This paper presents a method for distortion-optimized streaming of predictively coded video over packet networks with varying delay. In networks with significant delay variations, coded video frames can arrive late at the decoder and miss their respective display deadlines. Furthermore, due to predictive coding, a late frame can also prevent a number of subsequent frames from being displayed properly, where the number of affected frames or degree of distortion depends on the particular coding dependencies of the late frame. In this paper, we present an optimized video streaming strategy based on frame reordering for networks with significant delay variations. This streaming strategy minimizes distortion by exploiting the fact that different late frames result in different degrees of distortion. We model the router-induced delay in a wired network with an analytical PDF and we model the link-layer retransmission delay of a wireless network with the 3GPP specification for W-CDMA radio link control. We compute the distortion for different frame reorderings using the network delay models and a source model that accounts for the prediction dependencies of predictively coded video. Our optimized streaming strategies are shown to reduce the number of late frames by 14 to 23% for the situations examined.

Performance of a multiple description streaming media content delivery network

Content delivery networks (CDN) have been widely used to provide reduced delay and packet loss, fault tolerance, and improved scalability for Web content delivery. Additional benefits are provided for video streaming when one designs a streaming media CDN (SM-CDN) for either conventional single description (SD) or multiple description (MD) coding. Specifically, when precise network conditions and topology are known, simulations show that an MD-SM-CDN can provide 20 to 40% reduction in distortion over a conventional SD-SM-CDN, even when the underlying CDN is not designed with MD streaming in mind. This paper examines the performance of an MD-SM-CDN as a function of different network topologies and loss conditions, and compares it with a conventional SD-SM-CDN. This examination provides insight into an MD-SM-CDNs performance when knowledge of network topology and conditions is imprecise or uncertain. Our simulations indicate that an MD-SM-CDN can provide improved performance over a conventional SD-SM-CDN over a wide range of network topologies and loss conditions.

Loss-Resilient Coding of Texture and Depth for Free-Viewpoint Video Conferencing

Free-viewpoint video conferencing allows a participant to observe the remote 3D scene from any freely chosen viewpoint. An intermediate virtual viewpoint image is typically synthesized using two pairs of transmitted texture and depth maps from two neighboring captured viewpoints via depth-image-based rendering (DIBR). To maintain high quality of synthesized images, it is imperative to contain the adverse effects of network packet losses that may arise during texture and depth video transmission. Towards this goal, we develop an integrated approach that exploits the representation redundancy inherent in the multiple streamed videos-a voxel in the 3D scene visible to two captured views is sampled and coded twice in the two views. In particular, at the receiver we first develop an error concealment strategy that adaptively blends corresponding pixels in the two captured views during DIBR, so that pixels from the more reliable transmitted view are weighted more heavily. We then couple it with a sender-side optimization of reference picture selection (RPS) during real-time video coding, so that blocks containing pixel samples of voxels that are visible in both views are more error-resiliently coded in one view only, given adaptive blending will mitigate errors in the other view. Further, synthesized view distortion sensitivities to texture versus depth errors are analyzed, so that relative importance of texture and depth code blocks can be computed for system-wide RPS optimization. Finally, quantization parameter (QP) is adaptively selected per frame, optimally trading off source distortion due to compression with channel distortion due to potential packet losses. Experimental results show that the proposed scheme can outperform previous work by up to 2.9 dB at 5% packet loss rate.

Distortion chains for predicting the video distortion for general packet loss patterns

When designing a system for video communication over a lossy packet network, it is highly beneficial to have a mechanism for accurately predicting the mean-squared error (MSE) distortion that results from different packet loss patterns. The paper proposes a distortion chains model for accurately predicting the end-to-end distortion for different general packet loss patterns. The performance is examined using JVT/H.264 encoded video sequences and previous frame error concealment. It is shown that, for all tested sequences, the proposed model predicts the total distortion due to a packet loss pattern within a 10% error bound 80% of the time, as compared to the conventional additive approach which achieves the same accuracy less then 40% of the time.

Rate-distortion optimized application-level retransmission using streaming agent for video streaming over 3G wireless network

Feedback adaptation has been the basis for many media streaming schemes whereby the media being sent is adapted according to feedback information about the channel. Central to the success of such adaptive schemes, the feedback must (1) arrive in a timely manner, and (2) carry enough information to effect useful adaptation. We examine the use of feedback adaptation for media streaming in a 3G wireless network, where the media servers are located in wired networks while the clients are wireless. We argue that end-to-end feedback adaptation using only information provided by 3G standards is neither timely, nor contain enough information for media adaptation at the server. We then show how the introduction of an streaming agent (SA) at the junction of the wired and wireless network can be used to provide useful information in a timely manner for media adaptation.