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1. QoE Inference and Improvement Without End-Host Control

   https://doi.org/10.1109/SEC.2018.00011  

   Nikravesh, Ashkan; Chen, Qi Alfred; Haseley, Scott; Zhu, Xiao; Challen, Geoffrey; Mao, Z. Morley (October 2018, 2018 IEEE/ACM Symposium on Edge Computing (SEC))

   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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2. Understanding the Impact of Wi-Fi Configuration on Volumetric Video Streaming Applications

   https://doi.org/10.1145/3609395.3610599  

   Kulkarni, Umakant; Diab, Khaled; Aggarwal, Shivang; Cao, Lianjie; Ahmed, Faraz; Sharma, Puneet; Fahmy, Sonia (September 2023, EMS '23: Proceedings of the 2023 Workshop on Emerging Multimedia Systems)

   Emerging multimedia applications often use a wireless LAN (Wi-Fi) infrastructure to stream content. These Wi-Fi deployments vary vastly in terms of their system configurations. In this paper, we take a step toward characterizing the Quality of Experience (QoE) of volumetric video streaming over an enterprise-grade Wi-Fi network to: (i) understand the impact of Wi-Fi control parameters on user QoE, (ii) analyze the relation between Quality of Service (QoS) metrics of Wi-Fi networks and application QoE, and (iii) compare the QoE of volumetric video streaming to traditional 2D video applications. We find that Wi-Fi configuration parameters such as channel width, radio interface, access category, and priority queues are important for optimizing Wi-Fi networks for streaming immersive videos. 

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3. Drop the packets: using coarse-grained data to detect video performance issues

   https://doi.org/10.1145/3386367.3431294  

   Mangla, Tarun; Halepovic, Emir; Zegura, Ellen; Ammar, Mostafa (January 2020, Proceedings of ACM Conext)

   null (Ed.)

   Understanding end-user video Quality of Experience (QoE) is important for Internet Service Providers (ISPs). Existing work presents mechanisms that use network measurement data to estimate video QoE. Most of these mechanisms assume access to packet-level traces, the most-detailed data available from the network. However, collecting packet-level traces can be challenging at a network-wide scale. Therefore, we ask: "Is it feasible to estimate video QoE with lightweight, readily-available, but coarse-grained network data?" We specifically consider data in the form of Transport Layer Security (TLS) transactions that can be collected using a standard proxy and present a machine learning-based methodology to estimate QoE. Our evaluation with three popular streaming services shows that the estimation accuracy using TLS transactions is high (up to 72%) with up to 85% recall in detecting low QoE (low video quality or high re-buffering) instances. Compared to packet traces, the estimation accuracy (recall) is 7% (9%) lower but has up to 60 times lower computation overhead. 

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4. Toward QoE-based Routing Path Selection

   https://doi.org/10.1109/HPSR57248.2023.10147938  

   Kulkarni, Umakant; Chen, Yufeng; Melampy, Patrick; Fahmy, Sonia (June 2023, 2023 IEEE 24th International Conference on High Performance Switching and Routing (HPSR))

   The increasing popularity of video streaming and conferencing services have altered the nature of Internet traffic. In this paper, we take a first step toward quantifying the impact of this changing nature of traffic on the Quality of Experience (QoE) of popular video streaming and conferencing applications. We first analyze the traffic characteristics of these applications and of backbone links, and show how simple multipath routing may adversely impact application QoE. To mitigate this problem, we propose a new routing path selection approach, inspired by the TCP timeout computation algorithm, that uses both the average and variation of path load. Preliminary results show that this approach improves application QoE by on average 14% and packet latency by 11% for video streaming and conferencing applications, respectively. 

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5. Foresight: planning for spatial and temporal variations in bandwidth for streaming services on mobile devices

   https://doi.org/10.1145/3458305.3463384  

   Sethuraman, Manasvini; Sarma, Anirudh; Dhekne, Ashutosh; Ramachandran, Umakishore (July 2021, Proceedings of the 12th ACM Multimedia Systems Conference)

   null (Ed.)

   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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