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Short Video Characterization: Design, Implementation and Evaluation

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posted on 2023-08-21, 00:00 authored by Shangyue Zhu

Short videos have recently emerged as a popular form of User-Generated Content (UGC) within modern social media. This content, typically less than a minute long, is predominately produced in vertical orientation on smartphones. While still fundamentally streaming, short video delivery on mobile devices is distinctly characterized by the aggressive usage of pre-loading. While still fundamentally streaming, short video delivery on mobile devices is distinctly characterized by the aggressive usage of pre-loading portions of the next videos for a user. This pre-loading helps to overcome the varying dynamics of wireless connectivity and also allows for a nearly instant fast swiping to the next video. Furthermore, the prevalent usage pattern of rapid content swiping as enabled by pre-loading results in significant wastage by virtue of streaming buffers or pre-loads never being watched. Given the heavy cost of video and limited bandwidth afforded to wireless devices, managing the tradeoffs between user Quality of Experience (bitrate, video stalls, video start-up delay) and data wastage/browsing seamlessness is complex and multi-faceted. It is this problem that forms the foundation for this dissertation understanding the current state of short video and providing solutions that optimize these tradeoffs associated with short video delivery.

To accomplish that, this research first delves deeply into the selection of mobile video bitrates, presenting a low-consumption solution from a bandwidth prediction perspective. Next, this research critically evaluates the pivotal factors and design choices of existing short video platform pre-loading mechanisms, while also considering the impact on user Quality of Experience (QoE), bitrate, and perceived video quality. Finally, this research introduces a Kettle Buffer Loading (KBL) dynamic pre-loading algorithm. KBL is designed to sense network conditions and employ an adaptive pre-loading strategy, aiming to enhance video QoE while minimizing data wastage. The dissertation continues by evaluating KBL and state-of-the-art algorithms for short video delivery, carefully exploring the benefits and pitfalls of prediction modeling and vendor network scenarios.

History

Date Modified

2023-09-07

Defense Date

2023-08-17

CIP Code

  • 40.0501

Research Director(s)

Aaron D. Striegel

Committee Members

Douglas Thain Spyros Mastorakis Emir Halepovic

Degree

  • Doctor of Philosophy

Degree Level

  • Doctoral Dissertation

Alternate Identifier

1396269347

OCLC Number

1396269347

Program Name

  • Computer Science and Engineering

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