Network quality-of-service (QoS) does not always translate to user quality-of-experience (QoE). Consequently, knowledge of user QoE is desirable in several scenarios that have traditionally operated on QoS information. Examples include traffic management by ISPs and resource allocation by the operating system. But today these systems lack ways to measure user QoE. To help address this problem, we propose offline generation of per-app models mapping app-independent QoS metrics to app-specific QoE metrics. This enables any entity that can observe an app's network traffic-including ISPs and access points-to infer the app's QoE. We describe how to generate such models for many diverse apps with significantly different QoE metrics. We generate models for common user interactions of 60 popular apps. We then demonstrate the utility of these models by implementing a QoE-aware traffic management framework and evaluate it on a WiFi access point. Our approach successfully improves QoE metrics that reflect user-perceived performance. First, we demonstrate that prioritizing traffic for latency-sensitive apps can improve responsiveness and video frame rate, by 46% and 115%, respectively. Second, we show that a novel QoE-aware bandwidth allocation scheme for bandwidth-intensive apps can improve average video bitrate for multiple users by up to 23%.
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Foresight: planning for spatial and temporal variations in bandwidth for streaming services on mobile devices
Spatiotemporal variation in cellular bandwidth availability is well-known and could
affect a mobile user's quality of experience (QoE), especially while using bandwidth
intensive streaming applications such as movies, podcasts, and music videos during
commute. If such variations are made available to a streaming service in advance it
could perhaps plan better to avoid sub-optimal performance while the user travels
through regions of low bandwidth availability. The intuition is that such future knowledge
could be used to buffer additional content in regions of higher bandwidth availability
to tide over the deficits in regions of low bandwidth availability. Foresight is a
service designed to provide this future knowledge for client apps running on a mobile
device. It comprises three components: (a) a crowd-sourced bandwidth estimate reporting
facility, (b) an on-cloud bandwidth service that records the spatiotemporal variations
in bandwidth and serves queries for bandwidth availability from mobile users, and
(c) an on-device bandwidth manager that caters to the bandwidth requirements from
client apps by providing them with bandwidth allocation schedules. Foresight is implemented
in the Android framework. As a proof of concept for using this service, we have modified
an open-source video player---Exoplayer---to use the results of Foresight in its video
buffer management. Our performance evaluation shows Foresight's scalability. We also
showcase the opportunity that Foresight offers to ExoPlayer to enhance video quality
of experience (QoE) despite spatiotemporal bandwidth variations for metrics such as
overall higher bitrate of playback, reduction in number of bitrate switches, and reduction
in the number of stalls during video playback.
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- Award ID(s):
- 1909346
- NSF-PAR ID:
- 10298040
- Date Published:
- Journal Name:
- Proceedings of the 12th ACM Multimedia Systems Conference
- Page Range / eLocation ID:
- 227 to 240
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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