Microsoft Smooth Streaming [3], Apple HTTP Live Stream- ing (HLS) [4], and ... to [13] for a more detailed account of the study. ..... http://developer.apple.com.
Fixing Multi-Client Oscillations in HTTP-based Adaptive Streaming: A Control Theoretic Approach Xiaoqing Zhu, Zhi Li, Rong Pan, Joshua Gahm, and Hao Hu Cisco Systems Inc. 170 W Tasman Drive, San Jose, CA 95134
Abstract—In recent years, the technology for video delivery over the Internet is shifting towards a new paradigm: HTTPbased adaptive streaming (HAS). An HAS client receives video contents on a segment by segment basis via standard HTTP GET requests. It can dynamically change the rate and quality of the video in the presence of time-varying bandwidth changes. When multiple clients compete over a common bottleneck link, however, they often fail to converge to their respecitive fair share of bandwidth. This leads to constant oscillations in the received video quality. In this paper, we uncover the cause of such oscillations based on observations from large-scale test bed experiments. We then propose a novel client rate adaptation algorithm, which strives to stabilize the playout buffer at a reference level via a proportionalintegral controller (PIC). Test bed evaluation results confirm the effectiveness of the proposed PIC scheme and its superior performance over Microsoft Smooth Streaming.
I. I NTRODUCTION Consumption of video content over the Internet grows at a rapid pace. By the end of year 2011, video exceeded half of global consumer Internet traffic, and video-on-demand traffic will triple by 2016 [1]. A high percentage of these contents are consumed in web browsers, via HTTP delivery over TCP. YouTube, for instance, streams over 4 billion hours of video each month [2] using a technique called progressive download. Instead of streaming the video content packet by packet, or downloading an entire video file before playing it out, the client progressively downloads portions of the large video file via standard HTTP GET requests. To guard against future bandwidth fluctuations. It builds up a sufficiently large buffer — typically tens of seconds — before starting the video playout. With such an approach, video content providers can avoid the expense of dedicated streaming servers by employing standard HTTP web servers and caches. They can also build upon the prevalence of existing content delivery networks (CDNs) for large-scale distribution at relatively low costs. Although progressive download works well for delivering short video clips over stable networks, its performance tends to degrade in more dynamic environments, e.g., over wireless links. Mismatch in network bandwidth and video source rate can cause frequent client re-buffering, stuttered video playout, and unpleasant viewing experience. As smart phones and MMSP’13, Sept. 30 - Oct. 2, 2013, Pula (Sardinia), Italy. c 978-1-4799-0125-8/13/$31.00 2013 IEEE.
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,/#(%+&.()# φ for φ ∈ (0, π/2), we need κ < η/τ to ensure that the solution exists for ωc . It can then be checked numerically whether |G(jωc )| < 1 or not. As a concrete example, the stability region for (κ, η) is marked in blue for C = 4 Mbps and τ = 2 s in Fig. 10. The red line shows the upper bound of η = κτ .
[1] Cisco White Paper, “Cisco visual networking index - forecast and methodology, 2011-2016,” http://newsroom.cisco.com/dlls/index.html. [2] “Youtube traffic statistics,” http://www.youtube.com/t/press statistics. [3] A. Zambelli, “IIS Smooth Streaming Technical Overview,” http://www.microsoft.com. [4] Apple Inc., “HTTP Live Streaming Overview,” http://developer.apple.com. [5] MPEG, “ISO/IEC 23009-1:2012 Information technology – Dynamic adaptive streaming over HTTP (DASH) – Part 1: Media presentation description and segment formats,” 2012. [6] S. Akhshabi and A. C. Begen, “What happens when HTTP adaptive streaming players compete for bandwidth,” in Proc. ACM Workshop on Network and Operating System Support for Digital Audio and Video (NOSSDAV’12), Toronto, Ontario, Canada, Jun. 2012. [7] S. Akhshabi, A. C. Begen, and C. Dovrolis, “An Experimental Evaluation of Rate-adaptation Algorithms in Adaptive Streaming over HTTP,” in Proc. ACM Multimedia Systems Conference (MMSys’11), San Jose, CA, USA, Feb. 2011, pp. 157–168. [8] L. D. Cicco and S. Mascolo, “An Experimental Investigation of the Akamai Adaptive Video Streaming,” in Proc. USAB’10, 2010, pp. 447– 464. [9] C. Liu, I. Bouazizi, and M. Gabbouj, “Rate Adaptation for Adaptiv HTTP streaming,” in Proc. ACM Multimedia Systems Conference (MMSys’11), San Jose, CA, USA, Feb. 2011, pp. 169–174. [10] D. Jarnikov and T. Ozcelebi, “Client Intelligence for Adaptive Streaming Solutions,” EURASIP Journal on Signal Processing: Image Communication, Special Issue on Advances in IPTV Technologies, vol. 26, no. 7, pp. 378–389, Aug. 2011. [11] L. D. Cicco, S. Mascolo, and V. Palmisano, “Feedback control for adaptive live video streaming,” in Proc. ACM Multimedia Systems Conference (MMSys’11), San Jose, CA, USA, Feb. 2011, pp. 145–156. [12] C. Zhou, X. Zhang, L. Huo, and Z. Guo, “A control-theoretic approach to rate adaptation for dynamic HTTP streaming,” in Proc. SPIE Visual Communications and Image Processing (VCIP’12), San Diego, CA, USA, Nov. 2012, pp. 1 – 6. [13] Z. Li, X. Zhu, J. Gahm, R. Pan, H. Hu, A. C. Begen, and D. Oran, “Probe and adapt: Rate adaptation for HTTP video streaming at scale,” IEEE Journal on Selected Areas in Communications, 2013. [14] C. Hollot, V. Misra, D. Towsley, and W.-B. Gong, “On designing improved controllers for AQM routers supporting TCP flows,” in Proc. IEEE International Conference on Computer Communications (INFOCOM’01), Anchorage, AK, USA, Apr. 2001. [15] G. Franklin, J. D. Powell, and A. Emami-Naeini, Feedback Control of Dynamic Systems. NJ, USA: Prentice Hall, 2006.
