Video content in Live HTTP Adaptive Streaming (HAS) is typically encoded using a pre-defined, fixed set of bitrate-resolution pairs (termed Bitrate Ladder), allowing play-back devices to adapt to changing network conditions using an adaptive bitrate (ABR) algorithm. However, using a fixed one-size-fits-all solution when faced with various content complexities, heterogeneous network conditions, viewer device resolutions and locations, does not result in an overall maximal viewer quality of experience (QoE). Here, we consider these factors and design LALISA, an efficient framework for dynamic bitrate ladder optimization in live HAS. LALISA dynamically changes a live video session’s bitrate ladder, allowing improvements in viewer QoE and savings in encoding, storage, and bandwidth costs. LALISA is independent of ABR algorithms and codecs, and is deployed along the path between viewers and the origin server. In particular, it leverages the latest developments in video analytics to collect statistics from video players, content delivery networks and video encoders, to perform bitrate ladder tuning. We evaluate the performance of LALISA against existing solutions in various video streaming scenarios using a trace-driven testbed. Evaluation results demonstrate significant improvements in encoding computation (24.4%) and bandwidth (18.2%) costs with an acceptable QoE.

In this paper, we introduce a CMCD-Aware per-Device bitrate LADder construction (CADLAD) that leverages the Common Media Client Data (CMCD) standard to address the above issues. CADLAD comprises components at both client and server sides. The client calculates the top bitrate (tb) — a CMCD parameter to indicate the highest bitrate that can be rendered at the client — and sends it to the server together with its device type and screen resolution. The server decides on a suitable bitrate ladder, whose maximum bitrate and resolution are based on CMCD parameters, to the client device with the purpose of providing maximum QoE while minimizing delivered data. CADLAD has two versions to work in Video on Demand (VoD) and live streaming scenarios. Our CADLAD is client agnostic; hence, it can work with any players and ABR algorithms at the client. The experimental results show that CADLAD is able to increase the QoE by 2.6x while saving 71% of delivered data, compared to an existing bitrate ladder of an available video dataset. We implement our idea within CAdViSE — an open-source testbed for reproducibility.

Many applications and online services produce and deliver multimedia traffic over the Internet. Video streaming services with a rapidly growing desire for more resources to provide better quality, such as Ultra High Definition (UHD) 8K content, are on the list. The HTTP Adaptive Streaming (HAS) technique defines standard baselines for audio-visual content streaming to balance the delivered media quality and minimize defects in streaming sessions. On the other hand, video codecs development and standardization help the progress toward improving such services by introducing efficient algorithms and technologies. Versatile Video Coding (VVC) is one of the latest advancements in video encoding technology that is still not fully optimized and not supported on all available platforms. Mentioned optimization of the video codecs and supporting more platforms require years of research and development. This paper provides multiple test assets in the form of a dataset that facilitates the research and development of the stated technologies. Our open-source dataset comprises Dynamic Adaptive Streaming over HTTP (MPEG-DASH) packaged multimedia assets, encoded with Advanced Video Coding (AVC), High Efficiency Video Coding (HEVC), AOMedia Video 1 (AV1), and VVC. We provide our dataset with resolutions of up to 7680x4320 or 8K. Our dataset has a maximum media duration of 322 seconds, and we offer our MPEG-DASH packaged content with two segments lengths, 4 and 8 seconds.

Exponential growth in multimedia streaming traffic over the Internet motivates the research and further investigation of the user's perceived quality of such services. Enhancement of experienced quality by the users becomes more substantial when service providers compete on establishing superiority by gaining more subscribers or customers. Quality of Experience (QoE) enhancement would not be possible without an authentic and accurate assessment of the streaming sessions. HTTP Adaptive Streaming (HAS) is today's prevailing technique to deliver the highest possible audio and video content quality to the users. An end-to-end evaluation of QoE in HAS covers the precise measurement of the metrics that affect the perceived quality, eg. startup delay, stall events, and delivered media quality. Mentioned metrics improvements could limit the service's scalability, which is an important factor in real-world scenarios. In this study, we will investigate the stated metrics, best practices and evaluations methods, and available techniques with an aim to (i) design and develop practical and scalable measurement tools and prototypes, (ii) provide a better understanding of current technologies and techniques (eg. Adaptive Bitrate algorithms), (iii) conduct in-depth research on the significant metrics in a way that improvements of QoE with scalability in mind would be feasible, and finally (iv) provide a comprehensive QoE model which outperforms state-of-the-art models.

Adaptive bitrate (ABR) algorithms play a crucial role in delivering the highest possible viewer's Quality of Experience (QoE) in HTTP Adaptive Streaming (HAS). Online video streaming service providers use HAS - the dominant video streaming technique on the Internet - to deliver the best QoE for their users. A viewer's delight relies heavily on how the ABR of a media player can adapt the stream's quality to the current network conditions. QoE for video streaming sessions has been assessed in many research projects to give better insight into the significant quality metrics such as startup delay and stall events. The ITU Telecommunication Standardization Sector (ITU-T) P.1203 quality evaluation model allows to algorithmically predict a subjective Mean Opinion Score (MOS) by considering various quality metrics. Subjective evaluation is the best assessment method for examining the end-user opinion over a video streaming session's experienced quality. We have conducted subjective evaluations with crowdsourced participants and evaluated the MOS of the sessions using the ITU-T P.1203 quality model. This paper's main contribution is to investigate the correspondence of subjective and objective evaluations for well-known heuristic-based ABRs.

Adaptive video streaming systems typically support different media delivery formats, e.g., MPEG-DASH and HLS, replicating the same content multiple times into the network. Such a diversified system results in inefficient use of storage, caching, and bandwidth resources. The Common Media Application Format (CMAF) emerges to simplify HTTP Adaptive Streaming (HAS), providing a single encoding and packaging format of segmented media content and offering the opportunities of bandwidth savings, more cache hits, and less storage needed. However, CMAF is not yet supported by most devices. To solve this issue, we present a solution where we maintain the main advantages of CMAF while supporting heterogeneous devices using different media delivery formats. For that purpose, we propose to dynamically convert the content from CMAF to the desired media delivery format at an edge node. We study the bandwidth savings with our proposed approach using an analytical model and simulation, resulting in bandwidth savings of up to 20% with different media delivery format distributions. We analyze the runtime impact of the required operations on the segmented content performed in two scenarios: (i) the classic one, with four different media delivery formats, and (ii) the proposed scenario, using CMAF-only delivery through the network. We compare both scenarios with different edge compute power assumptions. Finally, we perform experiments in a real video streaming testbed delivering MPEG-DASH using CMAF content to serve a DASH and an HLS client, performing the media conversion for the latter one.

Attempting to cope with fluctuations of network conditions in terms of available bandwidth, latency and packet loss, and to deliver the highest quality of video (and audio) content to users, research on adaptive video streaming has attracted intense efforts from the research community and huge investments from technology giants. How successful these efforts and investments are, is a question that needs precise measurements of the results of those technological advancements. HTTP-based Adaptive Streaming (HAS) algorithms, which seek to improve video streaming over the Internet, introduce video bitrate adaptivity in a way that is scalable and efficient. However, how each HAS implementation takes into account the wide spectrum of variables and configuration options, brings a high complexity to the task of measuring the results and visualizing the statistics of the performance and quality of experience. In this paper, we introduce CAdViSE, our Cloud-based Adaptive Video Streaming Evaluation framework for the automated testing of adaptive media players. The paper aims to demonstrate a test environment which can be instantiated in a cloud infrastructure, examines multiple media players with different network attributes at defined points of the experiment time, and finally concludes the evaluation with visualized statistics and insights into the results.