Nam Pham Ngoc

Buffer-based bitrate adaptation for adaptive HTTP streaming

Live and on-demand video streaming has recently contributed a significant part of the Internet traffic. In order to guarantee the best possible user experience, multimedia service is required to support large fluctuations in different network conditions. One of the cost effective means for multimedia delivery nowadays is HTTP streaming. The key challenge of this technology is how to change adaptively video bitrates according to network variation and client buffer while bestowing upon users the best possible video quality. In this paper, we propose a novel rate-adaptation method on client side, which allows clients to estimate buffer level in the future, and thus achieving a balance between the need for high video quality and buffer stability. The experiments show that our method performs well under varying network conditions.

Modeling and experimenting combined smart sleep and power scaling algorithms in energy-aware data center networks

Abstract Recent surveys have shown that the energy consumption in a data center considerably contributes to its operation costs. A remarkable part of the large energy volume consumed in data centers today is due to the over-provisioning of such network resources as switches, links, and servers to meet the stringent requirements on reliability. Therefore performance and energy issues are important factors for the design of large multi-tier data centers that can support multiple services. However, the design, analysis, and experiments of such a large and complex system often suffer from the lack of appropriate experimental infrastructures. In this paper, we firstly propose a new energy saving scheme that combines smart sleeping and power scaling algorithms. An energy analysis model is then proposed to calculate the energy saving bounds in case of low and high traffic utilization. We also present a platform for in-depth analysis of energy-aware data center networks, which is a combination of hardware testbed and emulation. Based on OpenFlow technology, the experimental platform is designed to capture details of energy consumed by all network components such as links, ports, and switches under different scenarios. Analytical and emulation results show that the combined algorithm improves the energy saving under the varied traffic utilization.

Terminal QoS: advanced resource management for cost-effective multimedia appliances in dynamic contexts

Advanced multimedia applications such as those being developed within Ambient Intelligence typically share common characteristics, viz. the need to be able to access a wide variety of multimedia content using a heterogeneous communication and consumption infrastructure, in combination with low cost and low power requirements. The fact that a large variety of heterogeneous multimedia content has potentially to be dealt with (depending on user preferences and interaction) can lead to a cost inefficient overdimensioning of network and terminal resources. To tackle this issue, advanced resource management techniques are needed that make trade-offs on the fly to match the content bandwidth, the media coding and rendering complexity to the available network and terminal resources, while maximizing the overall perceived quality. This process is often referred to as Quality of Service (QoS) management. This paper illustrates the need to perform aspects of the overall QoS management on the terminal (Terminal QoS). The Terminal QoS for 3D graphics rendering on a software terminal (TriMedia set-top box) and reconfigurable platform is described in detail.

A new power profiling method and power scaling mechanism for energy-aware NetFPGA gigabit router

Today the ICT industry accounts for 2-4% of the worldwide carbon emissions that are estimated to double in a business-as-usual scenario by 2020. A remarkable part of the large energy volume consumed in the Internet today is due to the over-provisioning of network resources such as routers, switches and links to meet the stringent requirements on reliability. Therefore, performance and energy issues are important factors in designing gigabit routers for future networks. However, the design and prototyping of energy-efficient routers is challenging because of multiple reasons, such as the lack of power measurements from live networks and a good understanding of how the energy consumption varies under different traffic loads and switch/router configuration settings. Moreover, the exact energy saving level gained by adopting different energy-efficient techniques in different hardware prototypes is often poorly known. In this article, we first propose a measurement framework that is able to quantify and profile the detailed energy consumption of sub-components in the NetFPGA OpenFlow switch. We then propose a new power-scaling algorithm that can adapt the operational clock frequencies as well as the corresponding energy consumption of the FPGA core and the Ethernet ports to the actual traffic load. We propose a new energy profiling method, which allows studying the detailed power performance of network devices. Results show that our energy efficient solution obtains higher level of energy efficiency compared to some existing approaches as the upper and lower bounds of power consumption of the NetFPGA Openflow switch are proved to be 30% lower than ones of the commercial HP Enterprise switch. Moreover, the new switch architecture can save up to 97% of dynamic power consumption of the FPGA chip at lowest frequency mode.

Perceptual difference evaluation of video alternatives in adaptive streaming

Adaptivity, which is the most important requirement for any video streaming systems, is usually supported by creating multiple versions of video content at the server. A practical question in this context would be “Which versions should be generated to support adaptivity?” For this purpose, we investigate, through subjective tests, the number of JND (Just Noticeable Difference) levels existing in the practical quality range of streaming video content. It is found that the typical number of video versions is 4 to 7, among which most versions are located in low bitrate range. The results of this study give useful insights for streaming content providers to prepare a reasonable set of video versions.

Quality-driven bitrate adaptation method for HTTP live-streaming

In this paper, we focus on the bitrate adaptation issue of HTTP live video streaming. We first present a systematic approach that enables the client to choose the best video quality when deciding video bitrate for next several segments. After that, based on the concept of JND (Just Noticeable Difference), an adaptation method is proposed to meet the tradeoff between the requirements of buffer stability and smooth video quality. The experiment results show that our proposed method can provide smooth videos in terms of perceptual quality even under a small buffer size (i.e. 10s). To the best of our knowledge, this paper presents for the first time the way to use a perceptual quality metric for adaptivity in HTTP streaming.

High throughput FPGA architecture for corner detection in traffic images

Corner detection is the most computationally intensive step in vehicle tracking and vehicle speed estimation algorithms. In order to have real-time vehicle tracking for traffic surveillance applications, high speed architectures for corner detection are needed. This paper presents a high throughput FPGA architecture for detecting special features (corners in more detail) on traffic images which are captured by cameras. The module is implemented based on the FAST (Features from Accelerated Segment Test) algorithm with some modifications to be suitable for traffic images. The proposed architecture is able to reduce a great number of unnecessary detected corner points and maintain a high throughput of more than a thousand of 8-bit gray-scale images per second at 640 × 480 resolution. The resource usage is 21% lower than that of existing work, which allows the architecture to be implemented on almost all types of FPGA.

An evaluation of quality metrics for 360 videos

360 videos are becoming more and more popular on video streaming platforms. However, a good quality metric for 360 videos is still an open issue. In this work, we investigate both objective and subjective quality metrics for 360 videos. The goals are to understand the perceived quality range provided by existing mobile 360 videos and, especially, to identify appropriate objective quality metrics for 360 video communications. To that end, a subjective test is conducted in this study. Then, the relationship between objective quality and subjective quality is investigated. Especially, ten objective quality measures are computed, considering the coding distortion measurement, cross-format distortion measurement, and end-to-end distortion measurement. It is found that most of the objective quality measures are well correlated with subjective quality. Also, among the evaluated quality measures, PSNR is shown to be the most appropriate for 360 video communications.

A novel quality model for HTTP adaptive streaming

HTTP Adaptive Streaming (HAS) has become a popular trend for multimedia delivery nowadays. Because of throughput variations, video quality strongly fluctuates during a session. Therefore, a main challenge in HAS is how to evaluate the overall video quality of a session. In this paper, we explore the impact of quality variations on the perceptual quality of a video in HAS. We propose to use the histogram of segment quality values and the histogram of quality gradients in a session to model the overall video quality. Subjective test results show that the proposed model can capture the segment quality variations and accurately predict the overall quality of a session.

A Novel Adaptation Method for HTTP Streaming of VBR Videos over Mobile Networks

Recently, HTTP streaming has become very popular for delivering videos over the Internet. For adaptivity, a provider should generate multiple versions of a video as well as the related metadata. Various adaptation methods have been proposed to support a streaming client in coping with strong bandwidth variations. However, most of existing methods target at constant bitrate (CBR) videos only. In this paper, we present a new method for quality adaptation in on-demand streaming of variable bitrate (VBR) videos. To cope with strong variations of VBR bitrate, we use a local average bitrate as the representative bitrate of a version. A buffer-based algorithm is then proposed to conservatively adapt video quality. Through experiments in the mobile streaming context, we show that our method can provide quality stability as well as buffer stability even under very strong variations of bandwidth and video bitrates.