Cloud gaming platforms lower the access barriers to graphics-intensive games by rendering computationally heavy game scenes on cloud GPU servers and streaming them back to players as real-time video, which in turn places significant demands on carrier networks to deliver these video streams with high throughput, low latency and minimal packet loss. To achieve decent user experience, cloud gaming platforms adapt streaming behaviors based on network conditions and allow users to adjust their graphics settings. Knowing the level of game streaming adaptability offered by various cloud gaming providers is helpful for network operators to effectively provision network resources for subscriber satisfaction, and for game development community to incentivize cloud gaming providers to better optimize their streaming techniques. Toward this objective, we develop a systematic framework to assess the adaptability of a cloud gaming platform in reducing network demand for lower client graphics settings; and in adjusting streaming quality under constrained network conditions for smooth gaming experience. Focusing on four popular platforms (NVIDIA GFN, Xbox, PlayStation and Amazon Luna), we begin by empirically profiling and comparing how they adapt game streaming characteristics to various levels of client graphics settings and network conditions. Building on the insights, we develop our systematic assessment framework, which provides quantitative scores for both fine-grained metrics by processing labeled traffic traces, as well as aggregated scores tailored to an assessor’s preference. We showcase our quantitative assessments of the four platforms.

To tap into the growing market of cloud gaming, whereby game graphics is rendered in the cloud and streamed back to the user as a video feed, network operators are creating monetizable assurance services that dynamically provision network resources. However, without accurately measuring cloud gaming user experience, they cannot assess the effectiveness of their provisioning methods. Basic measures such as bandwidth and frame rate by themselves do not suffice, and can only be interpreted in the context of the game played and the player activity within the game. This paper equips the network operator with a method to obtain a real-time measure of cloud gaming experience by analyzing network traffic, including contextual factors such as the game title and player activity stage. Our method is able to classify the game title within the first five seconds of game launch, and continuously assess the player activity stage as being active, passive, or idle. We deploy it in an ISP hosting NVIDIA cloud gaming servers for the region. We provide insights from hundreds of thousands of cloud game streaming sessions over a three-month period into the dependence of bandwidth consumption and experience level on the gameplay contexts.

Internet Service Providers (ISPs) bear the brunt of being the first port of call for poor video streaming experience. ISPs can benefit from knowing the user's device type (e.g., Android, iOS) and software agent (e.g., native app, Chrome) to troubleshoot platform-specific issues, plan capacity and create custom bundles. Unfortunately, encryption and NAT have limited ISPs' visibility into user platforms across video streaming providers. We develop a methodology to identify user platforms for video streams from four popular providers, namely YouTube, Netflix, Disney, and Amazon, by analyzing network traffic in real-time. First, we study the anatomy of the connection establishment process to show how TCP/QUIC and TLS handshakes vary across user platforms. We then develop a classification pipeline that uses 62 attributes extracted from the handshake messages to determine the user device and software agent of video flows with over 96% accuracy. Our method is evaluated and deployed in a large campus network (mimicking a residential broadband network) serving users including dormitory residents. Analysis of 100+ million video streams over a four-month period reveals insights into the mix of user platforms across the video providers, variations in bandwidth consumption across operating systems and browsers, and differences in peak hours of usage.