A human-and-network aware encoding adaptation scheme for Remote Desktop Access

Remote desktop access (RDA) applications have become vital for users involved in tasks such as tele-commuting, distance learning, and remote instrumentation. To deliver optimum user quality of experience (QoE), existing RDA applications are "network-aware" i.e., they employ online encoding adaptation that is based on network quality of service (QoS) measurement between the client and server ends. In this paper, we propose and evaluate an online RDA encoding adaptation scheme that is "human-and-network aware" i.e., our novel adaptation considers quality of application (QoA) performance perceived by the user in addition to the network QoS measured by the application. Owing to our offline QoA performance modeling strategy that uses polynomial regression and bootstrap sampling, our scheme does not require input from actual users for the online encoding adaptation. Our performance validation results show that our proposed human-network-aware adaptation outperforms the network-aware adaptation in terms of user QoE for a wide-variety of degraded network QoS conditions.