Stefano Petrangeli

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.

A multi-agent Q-Learning-based framework for achieving fairness in HTTP Adaptive Streaming

HTTP Adaptive Streaming (HAS) is quickly becoming the de facto standard for Over-The-Top video streaming. In HAS, each video is temporally segmented and stored in different quality levels. Quality selection heuristics, deployed at the video player, allow dynamically requesting the most appropriate quality level based on the current network conditions. Todays heuristics are deterministic and static, and thus not able to perform well under highly dynamic network conditions. Moreover, in a multi-client scenario, issues concerning fairness among clients arise, meaning that different clients negatively influence each other as they compete for the same bandwidth. In this article, we propose a Reinforcement Learning-based quality selection algorithm able to achieve fairness in a multi-client setting. A key element of this approach is a coordination proxy in charge of facilitating the coordination among clients. The strength of this approach is three-fold. First, the algorithm is able to learn and adapt its policy depending on network conditions, unlike current HAS heuristics. Second, fairness is achieved without explicit communication among agents and thus no significant overhead is introduced into the network. Third, no modifications to the standard HAS architecture are required. By evaluating this novel approach through simulations, under mutable network conditions and in several multi-client scenarios, we are able to show how the proposed approach can improve system fairness up to 60% compared to current HAS heuristics.

HTTP/2-Based Methods to Improve the Live Experience of Adaptive Streaming

HTTP Adaptive Streaming (HAS) is today the number one video technology for over-the-top video distribution. In HAS, video content is temporally divided into multiple segments and encoded at different quality levels. A client selects and retrieves per segment the most suited quality version to create a seamless playout. Despite the ability of HAS to deal with changing network conditions, HAS-based live streaming often suffers from freezes in the playout due to buffer under-run, low average quality, large camera-to-display delay, and large initial/channel-change delay. Recently, IETF has standardized HTTP/2, a new version of the HTTP protocol that provides new features for reducing the page load time in Web browsing. In this paper, we present ten novel HTTP/2-based methods to improve the quality of experience of HAS. Our main contribution is the design and evaluation of a push-based approach for live streaming in which super-short segments are pushed from server to client as soon as they become available. We show that with an RTT of 300 ms, this approach can reduce the average server-to-display delay by 90.1% and the average start-up delay by 40.1%.

A learning-based algorithm for improved bandwidth-awareness of adaptive streaming clients

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for Over-The-Top video streaming. A HAS video consists of multiple segments, encoded at multiple quality levels. Allowing the client to select the quality level for every segment, a smoother playback and a higher Quality of Experience (QoE) can be perceived. Although results are promising, current quality selection heuristics are generally hard coded. Fixed parameter values are used to provide an acceptable QoE under all circumstances, resulting in suboptimal solutions. Furthermore, many commercial HAS implementations focus on a video-on-demand scenario, where a large buffer size is used to avoid play-out freezes. When the focus is on a live TV scenario however, a low buffer size is typically preferred, as the video play-out delay should be as low as possible. Hard coded implementations using a fixed buffer size are not capable of dealing with both scenarios. In this paper, the concept of reinforcement learning is introduced at client side, allowing to adaptively change the parameter configuration for existing rate adaptation heuristics. Bandwidth characteristics are taken into account in the decision process, thus allowing to improve the clients bandwidth-awareness. Focus in this paper is on actively reducing the average buffer filling, evaluating results for two heuristics: the Microsoft IIS Smooth Streaming heuristic and the QoE-driven Rate Adaptation Heuristic for Adaptive video Streaming by Petrangeli et al. We show that using the proposed learning-based approach, the average buffer filling can be reduced by 8.3% compared to state of the art, while achieving a comparable level of QoE.

An HTTP/2-Based Adaptive Streaming Framework for 360° Virtual Reality Videos

Virtual Reality (VR) devices are becoming accessible to a large public, which is going to increase the demand for 360° VR videos. VR videos are often characterized by a poor quality of experience, due to the high bandwidth required to stream the 360° video. To overcome this issue, we spatially divide the VR video into tiles, so that each temporal segment is composed of several spatial tiles. Only the tiles belonging to the viewport, the region of the video watched by the user, are streamed at the highest quality. The other tiles are instead streamed at a lower quality. We also propose an algorithm to predict the future viewport position and minimize quality transitions during viewport changes. The video is delivered using the server push feature of the HTTP/2 protocol. Instead of retrieving each tile individually, the client issues a single push request to the server, so that all the required tiles are automatically pushed back to back. This approach allows to increase the achieved throughput, especially in mobile, high RTT networks. In this paper, we detail the proposed framework and present a prototype developed to test its performance using real-world 4G bandwidth traces. Particularly, our approach can save bandwidth up to 35% without severely impacting the quality viewed by the user, when compared to a traditional non-tiled VR streaming solution. Moreover, in high RTT conditions, our HTTP/2 approach can reach 3 times the throughput of tiled streaming over HTTP/1.1, and consistently reduce freeze time. These results represent a major improvement for the efficient delivery of 360° VR videos over the Internet.

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.

Towards SVC-based Adaptive Streaming in information centric networks

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for video streaming services. In HAS, each video is segmented and stored in different qualities. The client can dynamically select the most appropriate quality level to download, allowing it to adapt to varying network conditions. As the Internet was not designed to deliver such applications, optimal support for multimedia delivery is still missing. Information Centric Networking (ICN) is a recently proposed disruptive architecture that could solve this issue, where the focus is given to the content rather than to end-to-end connectivity. Due to the bandwidth unpredictability typical of ICN, standard AVC-based HAS performs quality selection sub-optimally, thus leading to a poor Quality of Experience (QoE). In this article, we propose to overcome this inefficiency by using Scalable Video Coding (SVC) instead. We individuate the main advantages of SVC-based HAS over ICN and outline, both theoretically and via simulation, the research challenges to be addressed to optimize the delivered QoE.

Improving Virtual Reality Streaming using HTTP/2

The demand for 360° Virtual Reality (VR) videos is expected to grow in the near future, thanks to the diffusion of VR headsets. VR Streaming is however challenged by the high bandwidth requirements of 360° videos. To save bandwidth, we spatially tile the video using the H.265 standard and stream only tiles in view at the highest quality. The video is also temporally segmented, so that each temporal segment is composed of several spatial tiles. In order to minimize quality transitions when the user moves, an algorithm is developed to predict where the user is likely going to watch in the near future. Consequently, predicted tiles are also streamed at the highest quality. Finally, the server push in HTTP/2 is used to deliver the tiled video. Only one request is sent from the client; all the tiles of a segment are automatically pushed from the server. This approach results in a better bandwidth utilization and video quality compared to traditional streaming over HTTP/1.1, where each tile has to be requested independently by the client. We showcase the benefits of our framework using a prototype developed on a Samsung Galaxy S7 and a Gear VR, which supports both tiled and non-tiled videos and streaming over HTTP/1.1 and HTTP/2. Under limited bandwidth conditions, we demonstrate how our framework can improve the quality watched by the user compared to a non-tiled solution where all of the video is streamed at the same quality. This result represents a major improvement for the efficient streaming of VR videos.

An HTTP/2 Push-Based Approach for Low-Latency Live Streaming with Super-Short Segments

Over the last years, streaming of multimedia content has become more prominent than ever. To meet increasing user requirements, the concept of HTTP Adaptive Streaming (HAS) has recently been introduced. In HAS, video content is temporally divided into multiple segments, each encoded at several quality levels. A rate adaptation heuristic selects the quality level for every segment, allowing the client to take into account the observed available bandwidth and the buffer filling level when deciding the most appropriate quality level for every new video segment. Despite the ability of HAS to deal with changing network conditions, a low average quality and a large camera-to-display delay are often observed in live streaming scenarios. In the meantime, the HTTP/2 protocol was standardized in February 2015, providing new features which target a reduction of the page loading time in web browsing. In this paper, we propose a novel push-based approach for HAS, in which HTTP/2’s push feature is used to actively push segments from server to client. Using this approach with video segments with a sub-second duration, referred to as super-short segments, it is possible to reduce the startup time and end-to-end delay in HAS live streaming. Evaluation of the proposed approach, through emulation of a multi-client scenario with highly variable bandwidth and latency, shows that the startup time can be reduced with 31.2% compared to traditional solutions over HTTP/1.1 in mobile, high-latency networks. Furthermore, the end-to-end delay in live streaming scenarios can be reduced with 4 s, while providing the content at similar video quality.

An HTTP/2 push-based approach for SVC adaptive streaming

HTTP Adaptive Streaming (HAS) is the de facto standard for over-the-top video streaming. In HAS, video content is encoded at multiple quality levels and temporally divided into multiple segments. The client can select the quality level for every video segment, allowing smoother playback and a better Quality of Experience (QoE). Although results are promising, current solutions often suffer from high round-trip time (RTT) cycles in mobile networks. This is especially true for scalable video coding (SVC), where multiple requests are required to retrieve a single video segment. Meanwhile, the IETF has standardized the HTTP/2 protocol since February 2015, providing new features that allow a reduction of the page load time in Web browsing. In this paper, we propose a novel approach based on HTTP/2s server push feature to actively push the base layer of live, SVC-encoded content from server to client. This allows to eliminate one RTT cycle for every video segment, which has a significant impact on the users QoE. Evaluating the proposed approach, we show that compared with HTTP/1.1, an improvement of 65.42% can be achieved for the average video quality in high-RTT networks. Compared to an AVC-based solution, the freeze frequency and duration are reduced by 54.55% and 53.06% respectively, while the loss in video quality is limited to 4.51%. Since playout freezes should be avoided at the cost of a lower video quality, we conclude that the proposed approach beneficially impacts the users QoE.