Video Streaming:

Video streaming has become one of the most critical applications on the Internet. The demand for ondemand and live content (e.g., Netflix, Hulu, ESPN), as well as for user-generated content (e.g., YouTube) is increasing so rapidly that it is predicted that by 2019, 80% of the Internet traffic will be video, forming a $100 billion market. The key issues in video streaming involve novel scheduling algorithms for adptive-bitrate videos. Streaming video over cloud servers make the problem challenging. Further, 360-degree videos involve additional flexibilities of using head movement prediction for scheduling, which can be exploited.


Video Streaming over Cloud:

Cloud computing has changed the way many Internet services are provided and operated. Video-on-Demand (VoD) providers are increasingly moving their streaming services, data storage, and encoding software to cloud service providers. With the annual growth of global video streaming at a rate of 18.3%, cloud-based video has become an imperative feature of any successful business. The main challenges in video scheduling over cloud needs innovations for file placement, migration, scheduling of queues, dynamic bandwidth allocation, cache managementm and adaprive bit-rate quality decisions (as illustrated also in the figure alongside).


Optimized Robust Video Streaming Algorithms:

Mobile video has emerged as a dominant contributor to cellular traffic. It already accounts for around 40-55 percent of all cellular traffic and is forecast to grow by around 55 percent annually through 2021. While its popularity is on the rise, delivering high quality streaming video over cellular networks remains extremely challenging. In particular, the video quality under challenging conditions such as mobility and poor wireless channel qualities is sometimes unacceptably poor. Almost every viewer at some point in time can relate to experiences of choppy videos, stalls, etc. Not surprisingly, a lot of attention from both research and industry in the past decade has focused on the development of adaptive streaming techniques for video on demand that can dynamically adjust the quality of the video being streamed to the changes in network conditions. We formulate such streaming problem as an optimization problem. Even though the optimization is non-convex with discrete variables, we show that the problem can be optimally solved with a polynomial time algorithm. This key component can be used to give streaming decisions using bandwidth prediction which is updated after each chunk download (obtained as harmonic mean or crowd-sourced learning). Further extensions with multiple paths and multi-cast framework are also important. Figure alongside depicts the significant improvements achieved by our online algorithms MP-SVC and MPTCP-SVC for multiple paths over the different baselines in the literature including BBA, MSPlayer, and Festive.


Metrics and Adaptive Streaming Algorithms for 360-degree video streaming:

In the 360-degree immersive video, a user only views a part of the entire raw video frame based on her viewing direction (as shown alongside). However, today’s 360-degree video players always fetch the entire panoramic view regardless of users’ head movement, leading to significant bandwidth waste that can be potentially avoided. We propose a novel adaptive streaming scheme for 360-degree videos. The basic idea is to fetch the invisible portion of a video at the lowest quality based on users’ head movement prediction, and to adaptively decide the video playback quality for the visible portion based on bandwidth prediction. Doing both in a robust manner requires overcome a series of challenges, such as jointly considering the spatial and temporal domains, tolerating prediction errors, and achieving low complexity.

  • A. Ghosh and V. Aggarwal, "A Rate Adaptive Robust Algorithm for Tile-based 360-degree video streaming," Submitted.
  • A. Ghosh, V. Aggarwal, and F. Qian, "A Rate Adaptation Algorithm for Tile-based 360-degree Video Streaming," Submitted to ACM ToMPECS, Feb 2018.
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    Estimation of QoE for the users:

    Video and web applications are the most prominent applications in today's world. It is important to understand how radio network characteristics (such as signal strength, handovers, load, etc.) influence users' Quality-of-Experience (QoE). Understanding the relationship between QoE and network characteristics is a pre-requisite for cellular network operators to detect when and where degraded network conditions actually impact QoE. Unfortunately, cellular network operators do not have access to detailed server-side or client-side logs to directly measure QoE metrics, such as abandonment rate, stalls, and video/session length. We devise a machine-learning-based mechanism to infer web QoE metrics from network traces accurately. The image alongside depicts partial web downloads as a function of network attributes. Further, heavy tails in work loads (file sizes, flow lengths, service times, etc.) have significant negative impact on the performance of queues and networks. There has been attempts to model work loads in head or tail of the distribution. We find that logPH distribution models both the head and the tail, since these distributions have a power law tail and can approximate any distribution arbitrarily closely not just in the tail but in its entire range. The figure below compares the logPH fit of the famous Internet file size data of Crovella.

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