The main task of HTTP Adaptive Streaming is to adapt video quality dynamically under variable network conditions. This is a key feature for multimedia delivery especially when quality of service cannot be granted network-wide and, e.g., throughput may suffer short term fluctuations. Hence, robust bitrate adaptation schemes become crucial in order to improve video quality. The objective, in this context, is to control the filling level of the playback buffer and maximize the quality of the video, while avoiding unnecessary video quality variations. In this paper we study bitrate adaptation algorithms based on Backward-Shifted Coding (BSC), a scalable video coding scheme able to greatly improve video quality. We design bitrate adaptation algorithms that balance video rate smoothness and high network capacity utilization, leveraging both on throughput-based and buffer-based adaptation mechanisms. Extensive simulations using synthetic and real-world video traffic traces show that the proposed scheme performs remarkably well even under challenging network conditions.
The analysis reported in @cite_12 provides a complete characterization of BSC's performance with respect to a set of key metrics, namely the initial buffering delay, the playback interruption, and the mean video quality, which are responsible for the users' quality of experience (QoE). Also, a cost function is proposed in order to evaluate the QoE. However, the analysis proposed in @cite_12 does not account for the concurrent effect of bitrate adaptation. The scheme, combines video streaming and video downloading. Users are able to download optional enhancement layers for a tagged video. But, video streaming leverages on the base layer only. @cite_20 , numerical simulations demonstrate that system renders higher average video data rate than standard streaming schemes (HAS); the effect of adaptation part is neglected.
@cite_20 @cite_12
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Although HTTP-based video streaming can easily penetrate firewalls and profit from Web caches, the underlying TCP may introduce large delays in case of a sudden capacity loss. To avoid an interruption of the video stream in such cases we propose the Backward-Shifted Coding (BSC). Based on Scalable Video Coding (SVC), BSC adds a time-shifted layer of redundancy to the video stream such that future frames are downloaded at any instant. This pre-fetched content maintains a fluent video stream even under highly variant network conditions and leads to high Quality of Experience (QoE). We characterize this QoE gain by analyzing initial buffering time, re-buffering time and content resolution using the Ballot theorem. The probability generating functions of the playback interruption and of the initial buffering latency are provided in closed form. We further compute the quasi-stationary distribution of the video quality, in order to compute the average quality, as well as temporal variability in video quality. Employing these analytic results to optimize QoE shows interesting trade-offs and video streaming at outstanding fluency. A continuous evaluation of the end user’s quality-of-experience (QoE) is essential for efficient video streaming. This is crucial for networks with constrained resources that offer time-varying channel quality to its users. In hypertext transfer protocol-based video streaming, the QoE is measured by quantifying the perceptual impact of distortions caused by rate adaptation or interruptions in playback due to rebuffering events. The resulting impact on the QoE due to these distortions has been studied individually in the literature. However, the QoE is determined by an interplay of these distortions, and therefore necessitates a combined study of them. To the best of our knowledge, there is no publicly available database that studies these distortions jointly on a continuous time basis. In this paper, our contributions are twofold. First, we present a database consisting of videos at full high definition and ultrahigh definition resolutions. We consider various levels of rate adaptation and rebuffering distortions together in these videos as experienced in a typical realistic setting. A subjective evaluation of these videos is conducted on a continuous time scale. Second, we present a QoE evaluation framework comprising a learning-based model during playback and an exponential model during rebuffering. Furthermore, we perform an objective evaluation of popular video quality assessment and continuous time QoE metrics over the constructed database. The objective evaluation study demonstrates that the performance of the proposed QoE model is superior to that of the objective metrics. The database is publicly available for download at http: www.iith.ac.in lfovia downloads.html . Over-the-top mobile adaptive video streaming is invariably influenced by volatile network conditions, which can cause playback interruptions (stalling or rebuffering events) and bitrate fluctuations, thereby impairing users’ quality of experience (QoE). Video quality assessment models that can accurately predict users’ QoE under such volatile network conditions are rapidly gaining attention, since these methods could enable more efficient design of quality control protocols for media-driven services such as YouTube, Amazon, Netflix, and many others. However, the development of improved QoE prediction models requires data sets of videos afflicted with diverse stalling events that have been labeled with ground-truth subjective opinion scores. Toward this end, we have created a new mobile video quality database that we call LIVE Mobile Stall Video Database-II. Our database contains a total of 174 videos afflicted with distortions caused by 26 different stalling patterns. We describe the way we simulated the diverse stalling events to create a corpus of distorted videos, and we detail the human study we conducted to obtain continuous-time subjective scores from 54 subjects. We also present the outcomes of our comprehensive analysis of the impact of several factors that influence subjective QoE, and report the performance of existing QoE-prediction models on our data set. We are making the database (videos, subjective data, and video metadata) publicly available in order to help the advance state-of-the-art research on user-centric mobile network planning and management. The database may be accessed at http: live.ece.utexas.edu research LIVEStallStudy liveMobile.html . User-perceived quality-of-experience (QoE) is critical in Internet video applications as it impacts revenues for content providers and delivery systems. Given that there is little support in the network for optimizing such measures, bottlenecks could occur anywhere in the delivery system. Consequently, a robust bitrate adaptation algorithm in client-side players is critical to ensure good user experience. Previous studies have shown key limitations of state-of-art commercial solutions and proposed a range of heuristic fixes. Despite the emergence of several proposals, there is still a distinct lack of consensus on: (1) How best to design this client-side bitrate adaptation logic (e.g., use rate estimates vs. buffer occupancy); (2) How well specific classes of approaches will perform under diverse operating regimes (e.g., high throughput variability); or (3) How do they actually balance different QoE objectives (e.g., startup delay vs. rebuffering). To this end, this paper makes three key technical contributions. First, to bring some rigor to this space, we develop a principled control-theoretic model to reason about a broad spectrum of strategies. Second, we propose a novel model predictive control algorithm that can optimally combine throughput and buffer occupancy information to outperform traditional approaches. Third, we present a practical implementation in a reference video player to validate our approach using realistic trace-driven emulations.