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Many commercial video players rely on bitrate adaptation logic to adapt the bitrate in response to changing network conditions. Past measurement studies have identified issues with today’s commercial players with respect to three key metrics—efficiency, fairness, and stability—when multiple bitrate-adaptive players share a bottleneck link. Unfortunately, our current understanding of why these effects occur and how they can be mitigated is quite limited.

In this paper, we present a principled understanding of bitrate adaptation and analyze several commercial players through the lens of an abstract player model. Through this framework, we identify the root causes of several undesirable interactions that arise as a consequence of overlaying the video bitrate adaptation over HTTP. Building on these insights, we develop a suite of techniques that can systematically guide the tradeoffs between stability, fairness and efficiency and thus lead to a general framework for robust video adaptation. We pick one concrete instance from this design space and show that it significantly outperforms today’s commercial players on all three key metrics across a range of experimental scenarios.


Video traffic is becoming the dominant share of Internet traffic today. This growth in video is accompanied, and in large part driven, by a key technology trend: the shift from customized connection-oriented video transport protocols (e.g., RTMP) to HTTP-based adaptive streaming protocols. With an HTTP-based adaptive streaming protocol, a video player can dynamically (at the granularity of seconds) adjust the video bitrate based on the available network bandwidth. As video traffic is expected to dominate Internet traffic [5], the design of robust adaptive HTTP streaming algorithms is important not only for the performance of video applications, but also the performance of the Internet as a whole. Drawing an analogy to the early days of the Internet, a robust TCP was critical to prevent “congestion collapse”; we are potentially at a similar juncture today with respect to HTTP streaming protocols. Building on this high-level analogy, it is evident that the design of a robust adaptive video algorithm must look beyond single player view to account for the interactions across multiple adaptive streaming players that compete at bottleneck links.


  • A robust TCP was critical to prevent “congestion collapse”.
  • It fails to achieve one or more of efficiency, fairness, and stability properties when two players compete at a bottleneck link.
  • It worsens as the number of competing players increases.


We systematically study these problems through the lens of an abstract video player that needs to implement three key components (1) scheduling a specific video “chunk” to be downloaded, (2) selecting the bitrate for each chunk, and (3) estimating the bandwidth. At a high-level, the aforementioned problems arise as a result of overlaying the adaptation logic on top of several layers that may hide the true network state. Consequently, the feedback signal that the player receives from the network is not a true reflection of the network state. Furthermore, this feedback can also be biased by the decisions the player makes as well. Specifically, we observe that periodic chunk scheduling used in conjunction with stateless bitrate selection used by players today can lead to undesirable feedback

loops with bandwidth estimation and cause unnecessary bitrate switches and unfairness in the choice of bitrates. We leverage measurement-driven insights to design robust mechanisms for the three player components to overcome these biases. Our specific recommendations are (Section 3): (1) randomized chunk scheduling to avoid synchronization biases in sampling the network state, (2) a stateful bitrate selection that compensates for the biased interaction between bitrate and estimated bandwidth, (3) a delayed update approach to tradeoff stability and efficiency, and (4) a bandwidth estimator that uses the harmonic mean of download speed over recent chunks to be robust to outliers. Taken together, these approaches define a family of adaptation algorithms that vary in the tradeoff across fairness, efficiency, and stability. For example, we can consider player designs that choose the randomized scheduling with the stateful bitrate selection, without implementing the delayed update or the new bandwidth estimator.


  • It has robust mechanisms for chunk scheduling, bandwidth estimation, and bitrate selection.
  • It explores the design space of adaptive video algorithms with the goals of fairness, stability, and efficiency.



üProcessor                  -        Pentium –IV

üSpeed                        -        1.1 Ghz

üRAM                         -        512 MB(min)

üHard Disk                 -        40 GB

üKey Board                -        Standard Windows Keyboard

üMouse                       -        Two or Three Button Mouse

üMonitor                     -        LCD/LED


  • Operating system:         Windows XP
  • Coding Language:         Java
  • Data Base             :         MySQL
  • Tool                     :         Net Beans IDE


 Junchen Jiang,Vyas Sekar, Hui Zhang,“Improving Fairness, Efficiency, and Stability in HTTP-based Adaptive Video Streaming with FESTIVE”IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 22, NO. 1, Feb. 2014.

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