Te-Yuan Huang
Nick McKeown
ABSTRACT
Existing ABR algorithms face a significant challenge in estimating future capacity: capacity can vary widely over time, a phenomenon commonly observed in commercial services. In this work, we suggest an alternative approach: rather than presuming that capacity estimation is required, it is perhaps better to begin by using only the buffer, and then ask when capacity estimation is needed. We test the viability of this approach through a series of experiments spanning millions of real users in a commercial service. We start with a simple design which directly chooses the video rate based on the current buffer occupancy. Our own investigation reveals that capacity estimation is unnecessary in steady state; however using simple capacity estimation (based on immediate past throughput) is important during the startup phase, when the buffer itself is growing from empty. This approach allows us to reduce the rebuffer rate by 10-20% compared to Netflix's then-default ABR algorithm, while delivering a similar average video rate, and a higher video rate in steady state.
- S. Akhshabi et al. An Experimental Evaluation of Rate Adaptation Algorithms in Adaptive Streaming over HTTP. In ACM MMSys, 2011. Google Scholar
Digital Library
- S. Akhshabi et al. What Happens When HTTP Adaptive Streaming Players Compete for Bandwidth? In ACM NOSSDAV, 2012. Google ScholarDigital Library
- A. Balachandran et al. Analyzing the Potential Benefits of CDN Augmentation Strategies for Internet Video Workloads. In ACM IMC, October 2013. Google ScholarDigital Library
- A. Balachandran et al. Developing a Predictive Model of Quality of Experience for Internet Video. In ACM SIGCOMM, 2013. Google ScholarDigital Library
- L. D. Cicco et al. An Experimental Investigation of the Akamai Adaptive Video Streaming. In USAB, 2010. Google ScholarDigital Library
- L. D. Cicco et al. ELASTIC: a Client-side Controller for Dynamic Adaptive Streaming over HTTP (DASH). In IEEE Packet Video Workshop, 2013.Google ScholarCross Ref
- F. Dobrian et al. Understanding the Impact of Video Quality on User Engagement. In ACM SIGCOMM, 2011. Google ScholarDigital Library
- T.-Y. Huang et al. Confused, Timid, and Unstable: Picking a Video Streaming Rate is Hard. In ACM IMC, November 2012. Google ScholarDigital Library
- T.-Y. Huang et al. A Buffer-Based Approach to Video Rate Adaptation. Technical report, 2014. http://yuba.stanford.edu/~huangty/bba_report.pdf.Google Scholar
- J. Jiang et al. Improving fairness, efficiency, and stability in http-based adaptive video streaming with festive. In ACM CoNEXT, 2012. Google ScholarDigital Library
- S. S. Krishnan et al. Video Stream Quality Impacts Viewer Behavior: Inferring Causality Using Quasi-Experimental Designs. In ACM IMC, 2012. Google ScholarDigital Library
- Z. Li et al. Probe and adapt: Rate adaptation for http video streaming at scale. In http://arxiv.org/pdf/1305.0510.Google Scholar
- X. Liu et al. A Case for a Coordinated Internet Video Control Plane. In ACM SIGCOMM, 2012. Google ScholarDigital Library
- Y. Liu et al. User Experience Modeling for DASH Video. In IEEE Packet Video Workshop, 2013.Google Scholar
- R. Mok et al. QDASH: a QoE-aware DASH system. In ACM MMSys, 2012. Google ScholarDigital Library
- Sandvine: Global Internet Phenomena Report 2012 Q2. http://tinyurl.com/nyqyarq.Google Scholar
- Sandvine: Global Internet Phenomena Report 2013 H2. http://tinyurl.com/nt5k5qw.Google Scholar
- Netflix ISP Speed Index. http://ispspeedindex.netflix.com/.Google Scholar
- H. Sundaram, W.-C. Feng, and N. Sebe. Flicker Effects in Adaptive Video Streaming to Handheld Devices. In ACM MM, November 2011.Google Scholar
- G. Tian and Y. Liu. Towards Agile and Smooth Video Adaptation in Dynamic HTTP Streaming. In ACM CoNEXT, December 2012. Google ScholarDigital Library
- Private conversation with YouTube ABR team.Google Scholar
Index Terms
- A buffer-based approach to rate adaptation: evidence from a large video streaming service
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