QBR Metadata To Improve Streaming Efficiency and Quality

Cooper, William; Farrell, Sue; Subramanian, Kumar

doi:10.5594/m001784

Cited by 5 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unlike our work, this study does not perform an in-depth exploration of how their approach interacts with existing ABR rate adaptation algorithms. QBR [96] aims to improve the efficiency of existing ABR schemes by reducing the data usage while potentially increasing QoE.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Bitrate Streaming Over Cellular Networks: Rate Adaptation and Data Savings Strategies

Qin¹

2021

Preprint

View full text Add to dashboard Cite

Adaptive bitrate streaming (ABR) has become the de facto technique for video streaming over the Internet. Despite a flurry of techniques, achieving high quality ABR streaming over cellular networks remains a tremendous challenge. First, the design of an ABR scheme needs to balance conflicting Quality of Experience (QoE) metrics such as video quality, quality changes, stalls and startup performance, which is even harder under highly dynamic bandwidth in cellular network. Second, streaming providers have been moving towards using Variable Bitrate (VBR) encodings for the video content, which introduces new challenges for ABR streaming, whose nature and implications are little understood. Third, mobile video streaming consumes a lot of data. Although many video and network providers currently offer data saving options, the existing practices are suboptimal in QoE and resource usage. Last, when the audio and video tracks are stored separately, video and audio rate adaptation needs to be dynamically coordinated to achieve good overall streaming experience, which presents interesting challenges while, somewhat surprisingly, has received little attention by the research community. In this dissertation, we tackle each of the above four challenges.Firstly, we design a framework called PIA (PID-control based ABR streaming) that strategically leverages PID control concepts and novel approaches to account for the various requirements of ABR streaming. The evaluation results demonstrate that

show abstract

Section: Related Workmentioning

confidence: 99%

Adaptive Bitrate Streaming Over Cellular Networks: Rate Adaptation and Data Savings Strategies

Qin¹

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Unlike our work, this study does not perform an in-depth exploration of how their approach interacts with existing ABR rate adaptation algorithms. QBR [8] aims to improve the efficiency of existing ABR schemes by reducing the data usage while potentially increasing QoE. Specifically, a QBR server provides additional metadata hints to a client, allowing the client to request a reduced bitrate for chunks of low complexity.…”

Section: Related Workmentioning

confidence: 99%

Quality-aware strategies for optimizing ABR video streaming QoE and reducing data usage

Qin¹,

Hao

Pattipati³

et al. 2019

Proceedings of the 10th ACM Multimedia Systems Conference

View full text Add to dashboard Cite

Streaming videos over cellular networks is highly challenging. Since cellular data is a relatively scarce resource, many video and network providers offer options for users to exercise control over the amount of data consumed by video streaming. Our study shows that existing data saving practices for Adaptive Bitrate (ABR) videos are suboptimal: they often lead to highly variable video quality and do not make the most effective use of the network bandwidth. We identify underlying causes for this and propose two novel approaches to achieve better tradeoffs between video quality and data usage. The first approach is Chunk-Based Filtering (CBF), which can be retrofitted to any existing ABR scheme. The second approach is QUality-Aware Data-efficient streaming (QUAD), a holistic rate adaptation algorithm that is designed ground up. We implement and integrate our solutions into two video player platforms (dash.js and ExoPlayer), and conduct thorough evaluations over emulated/commercial cellular networks using real videos. Our evaluations demonstrate that compared to the state of the art, the two proposed schemes achieve consistent video quality that is much closer to the user-specified target, lead to far more efficient data usage, and incur lower stalls.

show abstract

DataPlanner

Qin¹,

Shende²,

Park³

et al. 2021

Proceedings of the 12th ACM Multimedia Systems Conference

View full text Add to dashboard Cite

This well-structured and well-explained paper presents DataPlanner (DP), which provides a client-sided non-intrusive module that interacts with the ABR logic of HTTP Adaptive Streaming systems to take data usage into account data usage when selecting the quality of retrieved video segments. This module enables clients, for example, to take limited per-session data budgets into account in the selection of video segments' quality without affecting the video quality to the same extent as earlier proposals. Addressing the issue of a user's total data budget would be an interesting extension to avoid DP's need for users' per-session decision making.DP adapts its decision to the actual consumed bandwidth and the size or quality video segments. The paper studies 2 adaptation approaches and considers that adaptation can be performed either by capping the qualities that are offered by the server (cap-beforehand) or the qualities that the client requests (cap-afterwards). The two adaptation approaches are called DP-Q and DP-T, where DP-Q bases its decisions on a segment quality metric (VMAF in the paper) and DP-T based them on segment complexity that was found in the literature (reference [36] in the paper).

show abstract

QBR Metadata To Improve Streaming Efficiency and Quality

Cited by 5 publications

References 0 publications

Adaptive Bitrate Streaming Over Cellular Networks: Rate Adaptation and Data Savings Strategies

Adaptive Bitrate Streaming Over Cellular Networks: Rate Adaptation and Data Savings Strategies

Quality-aware strategies for optimizing ABR video streaming QoE and reducing data usage

DataPlanner

Contact Info

Product

Resources

About