QoE-driven rate adaptation heuristic for fair adaptive video streaming

HTTP Adaptive Streaming (HAS) is quickly becoming the de facto standard for video streaming services. In HAS, each video is temporally segmented and stored in different quality levels. Rate adaptation heuristics, deployed at the video player, allow the most appropriate level to be dynamically requested, based on the current network conditions. It has been shown that today's heuristics underperform when multiple clients consume video at the same time, due to fairness issues among clients. Concretely, this means that different clients negatively influence each other as they compete for shared network resources. In this article, we propose a novel rate adaptation algorithm called FINEAS (Fair In-Network Enhanced Adaptive Streaming), capable of increasing clients' Quality of Experience (QoE) and achieving fairness in a multiclient setting. A key element of this approach is an in-network system of coordination proxies in charge of facilitating fair resource sharing among clients. The strength of this approach is threefold. First, fairness is achieved without explicit communication among clients and thus no significant overhead is introduced into the network. Second, the system of coordination proxies is transparent to the clients, that is, the clients do not need to be aware of its presence. Third, the HAS principle is maintained, as the in-network components only provide the clients with new information and suggestions, while the rate adaptation decision remains the sole responsibility of the clients themselves. We evaluate this novel approach through simulations, under highly variable bandwidth conditions and in several multiclient scenarios. We show how the proposed approach can improve fairness up to 80% compared to state-of-the-art HAS heuristics in a scenario with three networks, each containing 30 clients streaming video at the same time

HTTP/2-based adaptive streaming of HEVC video over 4G/LTE networks

In HTTP Adaptive Streaming, video content is temporally divided into multiple segments, each encoded at several quality levels. The client can adapt the requested video quality to network changes, generally resulting in a smoother playback. Unfortunately, live streaming solutions still often suffer from playout freezes and a large end-to-end delay. By reducing the segment duration, the client can use a smaller temporal buffer and respond even faster to network changes. However, since segments are requested subsequently, this approach is susceptible to high round-trip times. In this letter, we discuss the merits of an HTTP/2 push-based approach. We present the details of a measurement study on the available bandwidth in real 4G/LTE networks, and analyze the induced bit-rate overhead for HEVC-encoded video segments with a sub-second duration. Through an extensive evaluation with the generated video content, we show that the proposed approach results in a higher video quality (+7.5%) and a lower freeze time (-50.4%), and allows to reduce the live delay compared with traditional solutions over HTTP/1.1

dashc: a highly scalable client emulator for DASH video

In this paper we introduce a client emulator for experimenting with DASH video. dashc is a standalone, compact, easy-to-build and easy-to-use command line software tool. The design and implementation of dashc were motivated by the pressing need to conduct network experiments with large numbers of video clients. The highly scalable dashc has low CPU and memory usage. dashc collects necessary statistics about video delivery performance in a convenient format, facilitating thorough post hoc analysis. The code of dashc is modular and new video adaptation algorithm can easily be added. We compare dashc to a state-of-the art client and demonstrate its efficacy for large-scale experiments using the Mininet virtual network

Price-Based Controller for Utility-Aware HTTP Adaptive Streaming

HTTP Adaptive Streaming (HAS) permits to efficiently deliver video to multiple heterogenous users in a fully distributed way. This might however lead to unfair bandwidth utilization among HAS users. Therefore, network-assisted HAS systems have been proposed where network elements operate alongside with the clients adaptation logic for improving users satisfaction. However, current solutions rely on the assumption that network elements have full knowledge of the network status, which is not always realistic. In this work, we rather propose a practical network-assisted HAS system where the network elements infer the network link congestion using measurements collected from the client endpoints, the congestion level signal is then used by the clients to optimize their video data requests. Our novel controller maximizes the overall users satisfaction and the clients share the available bandwidth fairly from a utility perspective, as demonstrated by simulation results obtained on a network simulator

BBGDASH: A Max-Min Bounded Bitrate Guidance for SDN Enabled Adaptive Video Streaming.

The increase in video traffic and the end-user demands for high-quality videos have triggered academia and industry to find novel mechanisms for media distribution. Among the available streaming services, HTTP adaptive streaming (HAS) is being the de facto standard for multi-bitrate streaming. Recent studies show that the bitrate adaptation of client-driven HAS applications is challenging due to the fact that they are based on locally taken decisions for adapting the quality of the received video. Software-defined networking (SDN) has emerged as a new network paradigm to provide centralised management. The programmability and flexibility of SDN can be utilised to enhance the delivery of video over the Internet. In this paper, we present a novel and scalable network-assisted approach (denoted BBGDASH) that identifies the boundary range of the requested bitrate levels while preserving the final quality adaptation at the client. Experimental results demonstrate the potential of the proposed approach for delivering the video over SDN-enabled networks