Internet Video Projects (original) (raw)

Internet Video Homepage

Welcome to the Internet Video Homepage! This site is meant to provide a service to the community by summarizing the recent academic and industry efforts in the research and development of Internet video.

The site is still under development. Any comments and suggestions are welcomed and are critical to make this site up-to-date. Please send your comments (e.g., missing citations or industry developments) to "junchenj AT cs.cmu.edu"

Recent years have witnessed tremendous academic and industry efforts to understand and improve Internet video service. The Internet video is critical and challenging in four key aspects.

1. Massive traffic: Video traffic has emerged as the dominant traffic in today's Internet (more than 60% in North America in 2011 according to source from Akamai) and it is predicted to increase much faster than other applications in the years to come. Especially, streaming traffic (which consists of Live and video-on-demand streaming, but excludes the downloads of video content like P2P) counts for the biggest share in the whole video traffic (predicted to count for more than 90% Internet traffic by 2016 according to source from Cisco).
2. Heterogeneous access patterns: User access patterns vary greatly across different video site (Youtube vs. Netflix), time (prime vs. non-prime hours), content type (Live vs. video-on-demand). For example, videos on Youtube are typically much shorter than Netflix and usually viewers of Youtube are more dynamic than those of Netflix. Moreover, Live videos are more likely to cause flash crowd phenomenon than video-on-demand.
3. Unique quality metrics: Video imposes fundamentally different requirements on the delivery ecosystem than the existing web traffic and file downloads. For example, we know that overall completion time does not really reflect the actual user experience as it does not capture rebuffering-induced interruptions; users are sensitive to buffering as a 1% increase in buffering can lead to more than a 3 minutes reduction in expected viewing time
4. Fueled by recent technology trends: This growth in video is accompanied, and in large part driven, by several key technology trends: better integration with browser sandbox environment, the shift from customized connection-oriented video transport protocols (e.g., RTMP) to HTTP-based adaptive streaming protocols (e.g., Apple's HLS), and the shift from overlay network (e.g., P2P) multicast to the re-use of CDN infrastructure. In summary, high percent of video in Internet traffic mix has significant implications for Internet architecture.
   This site includes:
5. Overview of Internet video ecosystem
6. Industry platforms and standards
7. Academic Publications
8. Video traffic reports

Overview of Internet Video Ecosystem

There are several approaches to scale the video delivery through Internet, including CDN, P2P, IP multicast. Among them, using the existing CDN infrastructure emerges as the most popular way for Internet video streaming. This section presents a high-level overview of CDN-based video streaming and player design.

Figure 1.1 (from [Hui's tutorial]) shows a diagram of today's CDN-based Internet video delivery system. For both Live and Video-on-Demand streaming, CDN servers are used for storing, transferring and replicating content. Considering its ISP and broadband penetration, CDN will more and more influences the delivery speed and quality.

Figure 1.2 (from [FESTIVE slides]) shows the view from a video player's perspective. There are several key implications.

• The video player runs in browser environment.
• The video content is encoded at multiple discrete bitrates.
• The video player can switch bitrate at any time in the middle of a stream. For RTMP-based player, it is done by a proprietary protocol between server and client. For HTTP-based player, each bitrate stream is broken into multiple segments or "chunks". The i th chunk from one bitrate stream is aligned in the video time line to the i th chunk from another bitrate stream so that a video player can smoothly switch to a different bitrate at each chunk boundary.
• HTTP-based player enables server switching in the middle of a stream. Each video "chunk" is stored in multiple servers, and the video player can switch server at each chunk boundary by requesting from another server (even from another CDN or a cache).
• HTTP-based player is client-driven - The control logic (that decides bitrate and server) is run inside the player code, and it forms a separate control plane (instead of a proprietary protocol that combines data plane and control plane).

Industry platforms and standards.

How CDNs support video streaming

• Akamai: The Akamai Network: A Platform for High-Performance Internet Applications

• Akamai Live streaming: A Transport Layer for Live Streaming in a Content Delivery Network

• Youtube: Vivisecting YouTube: An Active Measurement Study

• Netflix: Unreeling Netflix: Understanding and Improving Multi-CDN Movie Delivery. BibTex

Standards for adaptive bitrate streaming

• Dynamic Adaptive Streaming over HTTP (DASH)

• CDN Federation

Major streaming protocols

• An Experimental Evaluation of Rate-Adaptation Algorithms in Adaptive Streaming over HTTP BibTex
  A methodology of blackbox study on player behavior and a study on adaptive player behavior in presence of bandwidth variance.

• Adobe Real-Time Messaging Protocol (RTMP) HTTP-based:

• Apple's HTTP Live Streaming (HLS)

• Microsoft SmoothStreaming

• Adobe HTTP Dynamic Streaming (HDS)

• Akamai's HD Network

• Netflix

• Hulu

Academic Publications on Internet Video

Overview (tutorial, keynotes, etc)

• "Internet Video Tutorial"
  Vyas Sekar (Stony Brook Univ.), Ion Stoica (UCB/Conviva), Hui Zhang (CMU/Conviva), SIGCOMM'2012
  • Evolution of Internet Video Ecosystem
  • How does video quality impact user engagement?
  • Understanding and Improving Video Quality
  • Future Directions
• "Internet Video: The 2011 Perspective"
  Keynote speech, Hui Zhang (CMU/Conviva), IWQoS'2011
• "Content Delivery Considerations for Different Types of Internet Video"
  Keynote speech, Leonidas Kontothanassis (Google), MMSys'2012
• "HTTP Adaptive Streaming in practice"
  Keynote speech, Mark Watson (Netflix), MMSys'2011

User experience measurement

• Understanding the impact of video quality on user engagement BibTex
  Study on the relationship between video quality metrics (e.g., buffering ratio, join time, average bitrate, etc) and user engagement (e.g, view duration).
• A quest for an internet video quality-of-experience metric BibTexDeveloping a Predictive Model of Quality-of-Experience for Internet Video
  Developing a QoE model that meets two key requirements (1) it has to be tied in to observable user engagement and (2) it should be actionable to guide practical system design decisions

Video traffic measurement

• I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system BibTex
• Nest: Measurements of Large-Scale Live VoD from the 2008 Olympics BibTex
• Network Characteristics of Video Streaming Traffic BibTex
• A Longitudinal View of HTTP Video Streaming Performance BibTex

Client-side performance measurement

• An Experimental Evaluation of Rate-Adaptation Algorithms in Adaptive Streaming over HTTP BibTex
• Confused, Timid, and Unstable: Picking a Video Streaming Rate is Hard BibTex

Design of adaptation player

• Improving Fairness, Efficiency, and Stability in HTTP-based Adaptive Video Streaming with FESTIVE BibTex
• Rate Adaptation for Adaptive HTTP Streaming BibTex
• Towards Agile and Smooth Video Adaptation in Dynamic HTTP Streaming BibTex
• Feedback Control for Adaptive Live Video Streaming BibTex

Server-side and network-level optimization

• Server-Based Traffic Shaping for Stabilizing Oscillating Adaptive Streaming Players BibTex

Cases for novel architecture

• A Case for a Coordinated Internet Video Control Plane BibTex
• Design and Deployment of a Hybrid CDN-P2P System for Live Video Streaming: Experiences with LiveSky BibTex

Theories and models

• Tradeoffs in CDN Designs for Throughput Oriented Traffic BibTex
• Optimizing Cost and Performance for Content Multihoming BibTex
• Towards Agile and Smooth Video Adaptation in Dynamic HTTP Streaming BibTex

Video traffic reports

Useful links:

• Global internet phenomena report
  Global traffic report. It highlights the traffic growth of real-time entertainment traffic (comprised mostly of streaming video and audio).

• Cisco Visual Networking Index: Forecast and Methodology, 2011-2016
  Traffic trend forecast from Cisco, in which video traffic is highlighted as one of the traffic sources that will grow faster than others.