HTTP Adaptive Streaming (HAS) technologies such as MPEG DASH are now used extensively to deliver television services to large numbers of viewers. In HAS, the client requests segments of content using HTTP, with an ABR algorithm selecting the quality at which to request each segment to trade-o video quality with the avoidance of stalling. This introduces signi cant end to end latency compared to traditional broadcast, due to the the client requiring a large enough bu er for the ABR algorithm to react to changes in network conditions in a timely manner. The recently standardised Common Media Application Format (CMAF) has helped address the issue of latency by de ning segments as composed of independently transferable chunks. In this paper, we describe a simulation model we have developed to evaluate the performance of four popular ABR algorithms using DASH and CMAF in various low latency live streaming scenarios. Realistic network conditions are used for the evaluation, which are based on throughput data taken from the CDN logs of a commercial live TV service. We quantify the performance of the ABR algorithms using a selection of QoE metrics, and show that CMAF can signi cantly improve ABR performance in low delay scenarios. CCS CONCEPTS• Information systems → Multimedia streaming.

In the recent years, HTTP Adaptive Bit Rate (ABR) streaming including Dynamic Adaptive Streaming over HTTP (DASH) has become the most popular technology for video streaming over the Internet. The client device requests segments of content using HTTP, with an ABR algorithm selecting the quality at which to request each segment to trade-off video quality with the avoidance of stalling. This introduces high latency compared to traditional broadcast methods, mostly in the client buffer which needs to hold enough data to absorb any changes in network conditions. Clients employ an ABR algorithm which monitors network conditions and adjusts the quality at which segments are requested to maximise the user's Quality of Experience. The size of the client buffer depends on the ABR algorithm's capability to respond to changes in network conditions in a timely manner, hence, low latency live streaming requires an ABR algorithm that can perform well with a small client buffer. In this paper, we present Llamaa new ABR algorithm specifically designed to operate in such scenarios. Our new ABR algorithm employs the novel idea of using two independent throughput measurements made over different timescales. We have evaluated Llama by comparing it against four popular ABR algorithms in terms of multiple QoE metrics, across multiple client settings, and in various network scenarios based on CDN logs of a commercial live TV service. Llama outperforms other ABR algorithms, improving the P.1203 Mean Opinion Score (MOS) as well as reducing rebuffering by 33% when using DASH, and 68% with CMAF in the lowest latency scenario.

HTTP Adaptive Streaming (HAS), the most prominent technology for streaming video over the Internet, suffers from high end-to-end latency when compared to conventional broadcast methods. This latency is caused by the content being delivered as segments rather than as a continuous stream, requiring the client to buffer significant amounts of data to provide resilience to variations in network throughput and enable continuous playout of content without stalling. The client uses an Adaptive Bitrate (ABR) algorithm to select the quality at which to request each segment to trade-off video quality with the avoidance of stalling to improve the Quality of Experience (QoE). The speed at which the ABR algorithm responds to changes in network conditions influences the amount of data that needs to be buffered, and hence to achieve low latency the ABR needs to respond quickly. Llama (Lyko et al. 28) is a new low latency ABR algorithm that we have previously proposed and assessed against four on-demand ABR algorithms. In this article, we report an evaluation of Llama that demonstrates its suitability for low latency streaming and compares its performance against three state-of-the-art low latency ABR algorithms across multiple QoE metrics and in various network scenarios. Additionally, we report an extensive subjective test to assess the impact of variations in video quality on QoE, where the variations are derived from ABR behaviour observed in the evaluation, using short segments and scenarios. We publish our subjective testing results in full and make our throughput traces available to the research community.

Video streaming continues to be the largest service delivered on the internet. This includes gaming videos, delivered both on-demand and live, where gaming footage is usually accompanied by a video of the player overlaid on top of the gameplay -resulting in Picture-In-Picture (PiP) content. Currently, PiP content is usually combined into a single video before being delivered to the client via technologies such as HTTP Adaptive Streaming (HAS). In this study, we investigated the QoE importance of gameplay and player elements in PiP gaming videos by varying the video quality of these elements individually. We conducted a subjective study, testing nine quality permutations based on three quality levels across three pieces of content from different gaming genres, with 30 participants recruited using an ethical crowdsourcing platform. We found that gameplay was significantly more important in terms of overall QoE, while the player element made a difference in only a few cases.

Recent multimedia experiences using techniques such as DASH allow the streaming delivery to be adapted to suit network context. Object Based Media (OBM) provides even more flexibility as distinct media objects are streamed and combined based on user preferences, allowing the experience to be personalised for the user. As adaptation can lead to degradation, modelling and measuring Quality of Experience (QoE) are crucial to ensure a perceptibly-optimal user experience. QoE models proposed for DASH include quality-related factors from single video-object streams and hence, are unsuitable for multi-video OBM experiences. In this paper, we propose an objective method to quantify QoE for video-based OBM experiences. Our model provides different strategies to aggregate individual object QoE contributions for different OBM experience genres. We apply our model to a case study and contrast it with the QoE levels obtained using a standard QoE model for DASH.