SDP-Based Quality Adaptation and Performance Prediction in Adaptive Streaming of VBR Videos

Recently, various adaptation methods have been proposed to cope with throughput fluctuations in HTTP adaptive streaming (HAS). However, these methods have mostly focused on constant bitrate (CBR) videos. Moreover, most of them are qualitative in the sense that performance metrics could only be obtained after a streaming session. In this paper, we propose a new adaptation method for streaming variable bitrate (VBR) videos using stochastic dynamic programming (SDP). With this approach, the system should have a probabilistic characterization along with the definition of a cost function that is minimized by a control strategy. Our solution is based on a new statistical model where the future streaming performance is directly related to the past bandwidth statistics. We develop mathematical models to predict and develop simulation models to measure the average performance of the adaptation policy. The experimental results show that the prediction models can provide accurate performance prediction which is useful in planning adaptation policy and that our proposed adaptation method outperforms the existing ones in terms of average quality and average quality switch

Optimal Multilayer Adaptation of SVC Video over Heterogeneous Environments

Scalable video coding (SVC) is a new video coding format which provides scalability in three-dimensional (spatio-temporal-SNR) space. In this paper, we focus on the adaptation in SNR dimension. Usually, an SVC bitstream may contain multiple spatial layers, and each spatial layer may be enhanced by several FGS layers. To meet a bitrate constraint, the fine-grained scalability (FGS) data of different spatial layers can be truncated in various manners. However, the contributions of FGS layers to the overall/collective video quality are different. In this work, we propose an optimized framework to control the SNR scalability across multiple spatial layers. Our proposed framework has the flexibility in allocating the resource (i.e., bitrate) among spatial layers, where the overall quality is defined as a function of all spatial layers' qualities and can be modified on the fly

Cumulative Quality Modeling for HTTP Adaptive Streaming

Thanks to the abundance of Web platforms and broadband connections, HTTP Adaptive Streaming has become the de facto choice for multimedia delivery nowadays. However, the visual quality of adaptive video streaming may fluctuate strongly during a session due to bandwidth fluctuations. So, it is important to evaluate the quality of a streaming session over time. In this paper, we propose a model to estimate the cumulative quality for HTTP Adaptive Streaming. In the model, a sliding window of video segments is employed as the basic building block. Through statistical analysis using a subjective dataset, we identify three important components of the cumulative quality model, namely the minimum window quality, the last window quality, and the average window quality. Experiment results show that the proposed model achieves high prediction performance and outperforms related quality models. In addition, another advantage of the proposed model is its simplicity and effectiveness for deployment in real-time estimation. The source code of the proposed model has been made available to the public at https://github.com/TranHuyen1191/CQM

Standard-Compliant Content Adaptation in IPTV Systems

IPTV, which is based on the universal IP infrastructure, has the inherent nature of heterogeneity in terms of content, networks, terminals, and users. An important solution to cope with such heterogeneity is content adaptation. This paper reviews the standardization issues related to content adaptation in IPTV standards. We first describe the basic architecture of content adaptation and its integration into the ITU-T IPTV architecture. Then typical methods of content adaptation in practical IPTV systems are discussed in detail. Especially, we highlight the standard metadata tools that are potential to support adaptation methods within ITU-T IPTV architecture. Some recent developments in other standard bodies are also discussed

AutoPruner: Transformer-Based Call Graph Pruning

Constructing a static call graph requires trade-offs between soundness and precision. Program analysis techniques for constructing call graphs are unfortunately usually imprecise. To address this problem, researchers have recently proposed call graph pruning empowered by machine learning to post-process call graphs constructed by static analysis. A machine learning model is built to capture information from the call graph by extracting structural features for use in a random forest classifier. It then removes edges that are predicted to be false positives. Despite the improvements shown by machine learning models, they are still limited as they do not consider the source code semantics and thus often are not able to effectively distinguish true and false positives. In this paper, we present a novel call graph pruning technique, AutoPruner, for eliminating false positives in call graphs via both statistical semantic and structural analysis. Given a call graph constructed by traditional static analysis tools, AutoPruner takes a Transformer-based approach to capture the semantic relationships between the caller and callee functions associated with each edge in the call graph. To do so, AutoPruner fine-tunes a model of code that was pre-trained on a large corpus to represent source code based on descriptions of its semantics. Next, the model is used to extract semantic features from the functions related to each edge in the call graph. AutoPruner uses these semantic features together with the structural features extracted from the call graph to classify each edge via a feed-forward neural network. Our empirical evaluation on a benchmark dataset of real-world programs shows that AutoPruner outperforms the state-of-the-art baselines, improving on F-measure by up to 13% in identifying false-positive edges in a static call graph.Comment: Accepted to ESEC/FSE 2022, Research Trac