With the next generation immersive video applications, network capacity is becoming a growing bottleneck to deliver a high quality video to end-users. Recent advances to tackle this challenge introduced super-resolution (SR) for video quality enhancement through neural computations by leveraging client-side compute capacity. However, the existing SR models are bulky, compute-, and memory-expensive, which makes it difficult to deploy them in practice. In this work, we present dcSR, a lightweight data-centric SR approach that enables a practical neural quality enhancement for videos. On the server-side, dcSR constructs micro SR models trained on a few selected frames from each video through a data-centric paradigm by employing a long term video scene understanding mechanism. On the client-side, dcSR integrates the micro SR models into the regular video decoder and enhances the video quality in real-time without compromising on quality enhancement. We evaluate dcSR and show its benefits by comparing it with previous methods.

In accordance with the recent enhancement of display technology, users demand a higher quality of streaming service, which escalates the bandwidth requirement. Considering the recent advent of high FPS (frame per second) 4K and 8K resolution 360° videos, such bandwidth concern further intensifies in 360° Virtual Reality (VR) content streaming even at a larger scale. However, the currently available bandwidth in most of the developed countries can hardly support the bandwidth required to stream such a scale of content. To address the mismatch between the demand on higher quality of streaming service and the saturated network improvement, we propose SALI360 that practically solves the mismatch by utilizing the characteristics of the human vision system (HVS). By pre-rendering a set of regions - where viewers are expected to fixate - on 360° VR content in higher quality than the other regions, SALI360 improves viewers' quality of perception (QoP) while reducing content size with geometry-based 360° content encoding. In our user experiment, we compare the performance of SALI360 to the existing 360° content-encoding techniques based on 20 viewers' head movement and eye gaze traces. To evaluate viewers' QoP, we propose FoL (field of look) that captures viewers' quality perception area in the visual focal field (8°) rather than a wide (around 90°) field of view (FoV). Results of our experimental 360° VR video streaming show that SALI360 achieves 53.3% of PSNR improvement in FoL, while gaining 9.3% of PSNR improvement in FoV. In addition, our subjective study on 93 participants verifies that SALI360 improves viewers' QoP in the 360° VR streaming service.

Crowdsourcing can be applied to provide scalable and efficient services to support various tasks. As the driving force of crowdsourcing is the interaction among participants, various incentive mechanisms have been proposed to attract and retain a sufficient number of participants to provide a sustainable crowdsourcing service. However, there exist some gaps between the modeled entities or markets in the existing works and those in reality: 1) dichotomous task valuation and workers' punctuality and 2) crowdsourcing service market monopolized by a platform. To bridge those gaps of such impractical assumption, we model workers' heterogeneous punctuality behavior and task depreciation over time. Based on those models, we propose an expected social welfare maximizing (ESWM) mechanism that aims to maximize the expected social welfare (ESW) by attracting and retaining more participants in the long term, i.e., multiple rounds of crowdsourcing. In the evaluation, we modeled the continuous competition between the ESWM and one of the existing works in both short-term and long-term scenarios. The simulation results show that the ESWM mechanism achieves higher ESW and platform utility than the benchmark by attracting and retaining more participants. Moreover, we prove that the ESWM mechanism achieves the desirable economic properties: individual rationality, budget balance, computational efficiency, and truthfulness.

Video streaming service has been essential to the Internet ecosystem since a majority of Internet contents is consumed via streaming ser-vices, such as Netflix and Youtube [3]. Moreover, contents providers started to upload 4K and even 8K videos on streaming service to satisfy users' demand for higher quality of streaming service in accordance with the recent refinement on display technology.