Video and audio trace files of pre-encoded video content for network performance measurements

Fitzek, Frank; Zorzi, Michele; Seeling, Patrick; Reisslein, Martin

doi:10.1109/ccnc.2004.1286866

Cited by 16 publications

(12 citation statements)

References 11 publications

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“…An alternative approach is to characterize the traffic of encoded video in the public domain, such as encoded videos available from YouTube, in video traces [22], [23]. The main drawbacks of video traces based on encoded public domain videos are that the exact encoding parameters are unknown and that video quality characterization with the widely used PSNR is impossible as the PSNR evaluation requires the uncompressed video source.…”

Section: Related Workmentioning

confidence: 99%

Video Transport Evaluation With H.264 Video Traces

Seeling

Reisslein

2012

IEEE Commun. Surv. Tutorials

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Abstract-The performance evaluation of video transport mechanisms becomes increasingly important as encoded video accounts for growing portions of the network traffic. Compared to the widely studied MPEG-4 encoded video, the recently adopted H.264 video coding standards include novel mechanisms, such as hierarchical B frame prediction structures and highly efficient quality scalable coding, that have important implications for network transport. This tutorial introduces a trace-based evaluation methodology for the network transport of H.264 encoded video. We first give an overview of H.264 video coding, and then present the trace structures for capturing the characteristics of H.264 encoded video. We give an overview of the typical video traffic and quality characteristics of H.264 encoded video. Finally, we explain how to account for the H.264 specific coding mechanisms, such as hierarchical B frames, in networking studies.

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Section: Related Workmentioning

confidence: 99%

Video Transport Evaluation With H.264 Video Traces

Seeling

Reisslein

2012

IEEE Commun. Surv. Tutorials

200

150

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“…While the video and audio streams are managed as separate RTP streams [38], the profile was set up to ensure that the ''application'' streams were launched concurrently rather than serially at the same time offset, for the same overall duration. Both streams were set up to commence streaming 60 s into the simulation.…”

Section: The Simulation Modelmentioning

confidence: 99%

Mobile WiMAX MAC and PHY layer optimization for IPTV

Hrudey

Trajković

2011

Mathematical and Computer Modelling

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a b s t r a c tWiMAX (Worldwide Interoperability for Microwave Access) embodies the IEEE 802.16 family of standards that provision wireless broadband access. With the IEEE 802.16e−2005 mobility amendment, WiMAX promises to address the ever-increasing demand for mobile high speed wireless data in fourth-generation (4G) networks. WiMAX market studies continue to project increased subscriber growth rates and planned carrier trials worldwide. Coupled with these increasing growth rates and higher WiMAX throughput rates, bandwidth intensive video on demand (VoD), Internet Protocol TV (IPTV), and mobile TV services are emerging in the forefront of the mobile arena.In this paper, we explore and derive optimum system level WiMAX parameters by quantifying network performance using such video-rich services. The video traffic will sufficiently load and stress the network to exploit the potential bandwidth, delay, and mobility limitations. We use the OPNET Modeler to engineer simulation sequences and explore the impact of channel bandwidth, time division duplex (TDD) frame size, advanced antenna systems support, and retransmission schemes using four objective performance metrics while streaming a feature-length movie to a Mobile WiMAX subscriber. The objective of this paper is to provide greater insight into Mobile WiMAX system performance using emerging, load intensive and delay sensitive media streaming services.

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“…One stream was observed to peak at 600 Mb/s for a few micro-seconds when it transferred only 3.1 Mb/s in 3 seconds. Fitzek et al [6] observed peak-to-mean values between 15 and 45 for bit-rates of audio/video streams. These studies indicated a high burstiness in traffic patterns for new streaming applications.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Complementing TCP Congestion Control with Forward Error Correction

et al. 2009

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Abstract. In this paper, we examine an emerging combination of challenges for TCP: increasingly bursty background traffic that is not subject to flow and congestion control, higher bandwidth networks and small buffers at network routers. As a result, TCP experiences short-term bursty packet losses that may not reflect long-term congestion. In this work, we propose a balanced approach that uses TCP congestion control mechanisms including Explicit Congestion Notification (ECN) to identify and overcome congestion, supported by adaptive FEC for short-term packet loss recovery due to bursty flows. We demonstrate the effectiveness of using FEC with TCP SACK congestion control by showing that such an approach improves performance for TCP flows for the emerging bursty traffic, small buffer, high bandwidth-delay product environments.

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Video and audio trace files of pre-encoded video content for network performance measurements

Cited by 16 publications

References 11 publications

Video Transport Evaluation With H.264 Video Traces

Video Transport Evaluation With H.264 Video Traces

Mobile WiMAX MAC and PHY layer optimization for IPTV

Complementing TCP Congestion Control with Forward Error Correction

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