> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1 Abstract— Video streaming over best-effort networks such as the Internet is now a significant application used by most Internet users. However, best-effort networks are characterized by dynamic and unpredictable changes in the available bandwidth, which adversely affect the quality of video. As such, it is important to have real-time detection mechanisms of bandwidth changes to ensure that video is adapted to the available bandwidth and transmitted at the highest quality. In this paper, we propose a Bayesian instantaneous end-to-end bandwidth change prediction model and method to detect and predict one-way bandwidth changes at the receiver. Unlike existing congestion detection mechanisms, which use network parameters such as packet loss probability, round trip time, or jitter, our approach uses weighted inter-arrival time of video packets at the receiver side. Furthermore, our approach is continuous, since it measures available bandwidth changes with each incoming video packet, and therefore detects congestion occurrence in less than 200 ms, on average, which is significantly faster than existing approaches. In addition, it is a one-way scheme, since it only takes into account the characteristics of the incoming path and not the outgoing path, as opposed to other approaches, which use round trip time and are hence less accurate. In this paper, we provide extensive experimental simulations and real-world network implementation. Our results indicate that the proposed detection method is superior to existing solutions. Index Terms— Congestion Control, VBR Video, Bayesian Statistics, High Definition Video Conferencing, Inter-arrival Time, Bandwidth Prediction, Bandwidth Change I. INTRODUCTION ODAY, video is streamed not only at its traditional resolutions of 340p or 480p, but also increasingly at higher resolutions, including High Definition (HD), which is now available in most video-on-demand services such as YouTube and Netflix. In addition to video-in-demand services, video conferencing systems are now featuring high- definition video. In a High Definition Video Conference (HDVC), individuals from various geographical locations simultaneously attend and contribute in a video conference session at HD quality. HDVCs are increasingly utilized by business personnel to accelerate decision-making processes and reduce traveling costs. In HDVC, the face to face connection is very important, and HDVC solutions must provide a degree of realism where a user is able to “read” other users from their facial expression. For example, a high ranking sales representative of a company may want to recognize any changes in the facial expression of the remote party upon receiving a price offer, in order to guess if the price was too high or too low. Eye contact, facial expression, and even slight twitches and other reactions must be visible in such scenarios. Therefore, an HDVC system with intolerable inter-frame latency or poor intra-frame quality is not acceptable for users attending those types of meetings as described above. To provide users with an acceptable level of satisfaction and to meet the aforementioned qualities, current HDVC systems [1] use dedicated networks with guaranteed quality of service (QoS). However, a dedicated network increases the total cost of the solution and is also a recurring cost. Hence, a solution that takes advantage of Internet’s best- effort services is preferred. This paper presents a novel solution mechanism, based on the Internet’s best-effort services, that performs timely detection of bandwidth changes to adapt video quality. We propose a Bayesian instantaneous end-to-end bandwidth change prediction model to detect and predict one-way bandwidth changes at the receiver. One of the main challenges of utilizing best effort networks, such as the Internet, for video streaming is to detect the network bandwidth modification and adjust the video bitrate variations [2]. For further illustration, let us consider an HDVC system between two locations over best-effort network as shown in the block diagram of Figure 1. The video content Continuous One-Way Detection of Available Bandwidth Changes for Video Streaming over Best Effort Networks Abbas Javadtalab, Mehdi Semsarzadeh, Aziz Khanchi, Shervin Shirmohammadi, Abdulsalam Yassine Distributed and Collaborative Virtual Environment Research Lab (DISCOVER Lab) School of Electrical Engineering and Computer Science (EECS), University of Ottawa, Canada {javadtalab, msemsarzadeh, akhanchi, shervin, ayassine}@discover.uottawa.ca T