Estimating WebRTC Video QoE Metrics Without Using Application Headers
Estimating WebRTC Video QoE Metrics Without Using Application Headers
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DOI：
10.1145/3618257.3624828
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发表时间：
2023-06
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影响因子：
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通讯作者：
Taveesh Sharma;Tarun Mangla;Arpit Gupta;Junchen Jiang;N. Feamster
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作者：
Taveesh Sharma;Tarun Mangla;Arpit Gupta;Junchen Jiang;N. Feamster
随着视频会议应用（VCA）的使用越来越多，VCA生态系统中的所有利益相关者，尤其是通常无法直接访问客户端软件的网络运营商，都必须了解并支持最终用户体验质量（QoE）。现有的VCA QoE估计方法使用应用级实时传输协议（RTP）报头的被动测量。然而，网络运营商并不总是能够在所有情况下访问RTP报头，特别是当VCA使用定制RTP协议（例如，缩放）或由于系统约束（例如，传统测量系统）。鉴于这一挑战，本文考虑在网络流量中使用更多标准功能，即IP和UDP报头，以提供关键VCA QoE指标（如帧速率和视频分辨率）的每秒估计值。我们开发了一种方法，该方法使用机器学习与流统计的组合（例如，吞吐量）和基于由VCA用于将视频帧分段成分组的机制导出的特征。我们评估了三种在WebRTC上运行的流行VCA的方法：Google Meet，Microsoft Teams和Cisco Webex。我们的评估包括从（1）受控实验室网络条件和（2）来自15个家庭的真实网络收集的54，696秒VCA数据。我们表明，基于ML的方法产生类似的准确性相比，基于RTP的方法，尽管只使用IP/UDP数据。例如，我们可以在实际数据中估计高达83.05%的1秒间隔内的FPS在2 FPS内，这仅比使用应用程序级RTP报头低1.76%。
The increased use of video conferencing applications (VCAs) has made it critical to understand and support end-user quality of experience (QoE) by all stakeholders in the VCA ecosystem, especially network operators, who typically do not have direct access to client software. Existing VCA QoE estimation methods use passive measurements of application-level Real-time Transport Protocol (RTP) headers. However, a network operator does not always have access to RTP headers in all cases, particularly when VCAs use custom RTP protocols (e.g., Zoom) or due to system constraints (e.g., legacy measurement systems). Given this challenge, this paper considers the use of more standard features in the network traffic, namely, IP and UDP headers, to provide per-second estimates of key VCA QoE metrics such as frames rate and video resolution. We develop a method that uses machine learning with a combination of flow statistics (e.g., throughput) and features derived based on the mechanisms used by the VCAs to fragment video frames into packets. We evaluate our method for three prevalent VCAs running over WebRTC: Google Meet, Microsoft Teams, and Cisco Webex. Our evaluation consists of 54,696 seconds of VCA data collected from both (1), controlled in-lab network conditions, and (2) real-world networks from 15 households. We show that the ML-based approach yields similar accuracy compared to the RTP-based methods, despite using only IP/UDP data. For instance, we can estimate FPS within 2 FPS for up to 83.05% of one-second intervals in the real-world data, which is only 1.76% lower than using the application-level RTP headers.