Abstract-Today, the technology for video streaming over the Internet is converging towards a paradigm named HTTPbased adaptive streaming (HAS), which brings two new features. First, by using HTTP/TCP, it leverages network-friendly TCP to achieve both firewall/NAT traversal and bandwidth sharing. Second, by pre-encoding and storing the video in a number of discrete rate levels, it introduces video bitrate adaptivity in a scalable way so that the video encoding is excluded from the closed-loop adaptation. A conventional wisdom is that the TCP throughput observed by an HAS client indicates the available network bandwidth, and thus can be used as a reliable reference for video bitrate selection.We argue that this is no longer true when HAS becomes a substantial fraction of the total traffic. We show that when multiple HAS clients compete at a network bottleneck, the presence of competing clients and the discrete nature of the video bitrates together result in difficulty for a client to correctly perceive its fair-share bandwidth. Through analysis and test bed experiments, we demonstrate that this fundamental limitation leads to, for example, video bitrate oscillation that negatively impacts the video viewing experience. We therefore argue that it is necessary to design at the application layer using a "probeand-adapt" principle for HAS video bitrate adaptation, which is akin to, but also independent of the transport-layer TCP congestion control. We present PANDA -a client-side rate adaptation algorithm for HAS -as practical embodiment of this principle. Our test bed results show that compared to conventional algorithms, PANDA is able to reduce the instability of video bitrate selection by over 75% without increasing the risk of buffer underrun.

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With an increasing demand for high-quality video content over the Internet, it is becoming more likely that two or more adaptive streaming players share the same network bottleneck and compete for available bandwidth. This competition can lead to three performance problems: player instability, unfairness between players, and bandwidth underutilization. However, the dynamics of such competition and the root cause for the previous three problems are not yet well understood. In this paper, we focus on the problem of competing video players and describe how the typical behavior of an adaptive streaming player in its Steady-State, which includes periods of activity followed by periods of inactivity (ON-OFF periods), is the main root cause behind the problems listed above. We use two adaptive players to experimentally showcase these issues. Then, focusing on the issue of player instability, we test how several factors (the ON-OFF durations, the available bandwidth and its relation to available bitrates, and the number of competing players) affect stability.

In this survey, we present state-of-the-art bitrate adaptation algorithms for HTTP adaptive streaming (HAS). As a key distinction from other streaming approaches, the bitrate adaptation algorithms in HAS are chiefly executed at each client, i.e., in a distributed manner. The objective of these algorithms is to ensure a high quality of experience (QoE) for viewers in the presence of bandwidth fluctuations due to factors like signal strength, network congestion, network reconvergence events, etc. While such fluctuations are common in public Internet, they can also occur in home networksor even managed networks where there is often admission control and QoS tools. Bitrate adaptation algorithms may take factors like bandwidth estimations, playback buffer fullness, device features, viewer preferences, and content features into account, albeit with different weights. Since the viewer's QoE needs to be determined in real-time during playback, objective metrics are generally used including number of buffer stalls, duration of startup delay, frequency and amount of quality oscillations, and video instability. By design, the standards for HAS do not mandate any particular adaptation algorithm, leaving it to system builders to innovate and implement their own method. This survey provides an overview of the different methods proposed over the last several years.

Prior work has shown that two or more adaptive streaming players can be unstable when they compete for bandwidth. The root cause of the instability problem is that, in Steady-State, a player goes through an ON-OFF activity pattern in which it overestimates the available bandwidth. We propose a server-based traffic shaping method that can significantly reduce such oscillations without significant (or any) loss in bandwidth utilization. The shaper is only activated when oscillations are detected, and it dynamically adjusts the shaping rate so that the player should ideally receive the highest available video profile while being stable. We evaluate the proposed method experimentally in terms of instability and utilization comparing with the unshaped case, under several scenarios.