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Since the outbreak of the COVID-19 pandemic, videoconferencing has become the default mode of communication in our daily lives at homes, workplaces and schools, and it is likely to remain an important part of our lives in the post-pandemic world. Despite its significance, there has not been any systematic study characterizing the user-perceived performance of existing videoconferencing systems other than anecdotal reports. In this paper, we present a detailed measurement study that compares three major videoconferencing systems: Zoom, Webex and Google Meet. Our study is based on 62 hours’ worth of more than 1.1K videoconferencing sessions, which were created with a mix of emulated videoconferencing clients deployed in the cloud, as well as real mobile devices running from a residential network over two separate periods with nine months apart. We find that the existing videoconferencing systems vary in terms of geographic scope and resource provisioning strategies, which in turns determine streaming lag experienced by users. We also observe that streaming rate can change under different conditions (e.g., available bandwidth, number of users in a session, mobile device status), which affects user-perceived streaming quality. Beyond these findings, our measurement methodology enables reproducible benchmark analysis for any types of comparative or longitudinal study on available videoconferencing systems. [ABSTRACT FROM AUTHOR]

Behavioral targeting of content to users is a huge and lucrative business, valued as a $20 billion industry that is growing rapidly. So far, the dominant players in this field like Google and Yahoo! examine the user requests coming to their servers and place appropriate ads based on the user's search keywords. Triple-play service providers have access to all the traffic generated by the users and can generate more comprehensive profiles of users based on their TV, broadband, and mobile usage. Using such multisource profile information, they can generate new revenue streams by smart targeting of ads to their users over multiple screens (computer, TV, and mobile handset). This paper proposes methods to place targeted ads to a TV based on user's interests. It proposes an ad auction model that can leverage multisource profile and can handle dynamic profile-based targeting like Google's AdWords vis-à-vis static demography-based targeting of legacy TV. We then present a 0.502-competitive revenue maximizing scheduling algorithm that chooses a set of ads in each time slot and assigns users to one of these selected ads. [ABSTRACT FROM PUBLISHER]