Aravindan Raghuveer

A Network-Aware Approach for Video and Metadata Streaming

Providing quality of service (QoS) for Internet-based video streaming applications requires the server and/or client to be network-aware and adaptive. We present a dynamic rate and quality adaptation algorithm where the server varies its sending rate (without varying the quality level) to adapt to the network and client conditions and only as a last resort, does quality adaptation. We place the adaptation logic at the client since it has better knowledge about both the demand (buffer conditions, variable bit-rate requirements) and supply (network conditions). Our approach is unique because the servers sending rate is calculated based on the clients varying demand (consumption rate) and the network status. Also, we do not model the network as a black-box but instead augment endpoint observations with a feedback from the network to represent its status more precisely. To make an informed adaptation decision, the client requires sizes of all frames in the variable bit rate video. But the overhead involved in sending this metadata is significant. So we propose a lossy compression technique to reduce the amount of control information and consequently the overhead. We also present a scheduling algorithm, dynamic scheduling algorithm for reduced trace delivery (DART), to deliver the compressed control information to the client. This algorithm can be used to deliver any form of metadata (like subtitles, alerts, etc.), especially in applications like IP-TV. Simulations show that the proposed techniques can significantly improve user perceived QoS when compared to other popular adaptation methods.

Towards efficient search on unstructured data: an intelligent-storage approach

Applications that create and consume unstructured data have grown both in scale of storage requirements and complexity of search primitives. We consider two such applications: exhaustive search and integration of structured and unstructured data. Current block-based storage systems are either incapable or inefficient to address the challenges bought forth by the above applications. We propose a storage framework to efficiently store and search unstructured and structured data while controlling storage management costs. Experimental results based on our prototype show that the proposed system can provide impressive performance and feature benefits.

Enabling database-aware storage with OSD

The ANSI object-based storage device (OSD) standard is a major step toward enabling explicit application-awareness in storage systems behind a standard, fully- interoperable interface [3]. In this paper, we explore a particular flavor of application-awareness, that of database applications. We describe the design and implementation of a database-aware storage system that uses the OSD interface not only as a means to access data, but also to permit explicit communication between the application and the storage system. This communication is significant, as it enables our storage system to transparently optimize data placement and request scheduling. We demonstrate that OSD makes it practical to improve storage performance in these ways without exposing proprietary disk drive parameters to application code, and without labor-intensive, fragile parameter measurement.

On providing reliability guarantees in live video streaming with collaborative clients

Although many overlay and P2P approaches have been proposed to assist large-scale live video streaming, how to ensure service quality and reliability still remains a challenging issue. Peer dynamics, especially unscheduled node departures, affect the perceived video quality at a peer node in two ways. In particular, the amplitude of quality fluctuations and the duration for which stable quality video is available at a node heavily depend on the nature of peer departures in the system. In this paper, we first propose a service quality model to quantify the quality and stability of a video stream in a P2P streaming environment. Based on this model, we further develop tree construction algorithms that ensure that every peer in the collaborative network receives a video stream with a statistical reliability guarantee on quality. A key advantage of the proposed approach is that we can now explicitly control the quality and stability of the video stream supplied to every node. This is the fundamental difference of our approach from existing approaches that provide stream stability by over-provisioning resources allocated to every peer. Also, the proposed tree construction schemes decide the position of a node in the delivery tree based on both its estimated reliability and upstream bandwidth contribution while striving to minimize the overall load on the server. Our simulations show that our algorithms use the server resources very efficiently while significantly improving the video stability at peers.

Network-aware rate adaptation for video streaming

Since the current Internet provides a best-effort service, timely delivery of data is not guaranteed for video streaming applications. Providing quality of service (QoS) in such a setting requires server and client to be network-aware and adaptive. We present a dynamic rate and quality adaptation scheme where the server varies its sending rate (without varying the quality) to adapt to the network and client conditions, and, only as a last resort, does quality adaptation. Our approach is unique because the servers sending rate is calculated based on the clients varying demand, and the network status. We do not model the network as a black-box but instead augment endpoint observations with feedback from the network and hence represent its status more precisely. We also propose a method to reduce the amount of control information needed to make adaptation decisions

StatStream: providing statistical reliability guarantees in peer-to-peer live video streaming

Although many large-scale P2P streaming techniques have been proposed, ensuring video reliability remains a challenging issue. We present a P2P streaming framework called StatStream that provides statistical guarantees on video quality and stability. We first propose a model to quantify video quality and stability. We then develop graph construction algorithms that ensure statistical guarantee on video quality at every peer. A key advantage of the proposed approach is that video quality and stability can be explicitly controlled. Simulations show that the proposed algorithms use the server resources efficiently while significantly improving the video stability at peers.