Jiro Katto

Adaptive Bit-rate Video Streaming on Named Data Networking with Active Throughput Estimation

Named Data Networking (NDN) has been recently introduced as a new future network architecture. The end-to-end throughput estimation for adaptive bit-rate video streaming on NDN is one of the most challenging topics. Specifically, the end-to-end throughput estimation on NDN appears to be unreliable, since the provider of content is unknown to the consumer. Moreover, partial caching on NDN routers Content Store could temporarily lead to packet loss due to throughput overestimation. In this paper, we present an active Interest adaptation scheme which operates by proactively estimating the throughput in a hop-by-hop fashion. The consumer node is then assisted with the most recent available end-to-end bandwidth. Therefore, the video player can promptly adapt to the change of network condition. The implementation evaluation using NDN-JS and DASH-JS on the setup network demonstrate that our proposed solution provides better average stream bit-rate and consumes less network bandwidth than the traditional system.

A History-Based TCP Throughput Prediction Incorporating Communication Quality Features by Support Vector Regression for Mobile Network

Throughput prediction is one of good solutions to improve quality of mobile applications (e.g., YouTube or Netflix) for video streaming delivery services in mobile networks. This is because such applications require monitoring the network performances to control content quality, thus guarantee quality of service (QoS) and quality of experience (QoE). In this paper, we propose a history-based TCP throughput prediction method incorporating communication quality features using SVR (Support Vector Regression). By taking history of communication quality features such as historical throughput and Received Signal Strength Indication (RSSI) into consideration, the throughput prediction error can be decreased. We conduct experiments with the proposed method and compare the prediction accuracy with a variety of methods in different scenarios of various moving modes of users. Results show that the proposed model could predict throughput effectively in various scenarios and decrease throughput prediction errors by a maximum of 26.47% compared with other methods.