Streaming Optimization in Web Engine-based Video Player for Television Application
Hoang, Minh (2025)
2025
Tietojenkäsittelyopin maisteriohjelma - Master's Programme in Computer Science
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
Hyväksymispäivämäärä
2025-06-30
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202506267442
https://urn.fi/URN:NBN:fi:tuni-202506267442
Tiivistelmä
This thesis investigates optimization techniques for web engine-based video players specifically designed for Smart TV platforms. As streaming becomes the dominant form of television content delivery, the technical challenges of delivering high-quality video experiences on resource-constrained Smart TV hardware have become increasingly significant. This research focuses on the optimization of streaming technologies, particularly HTTP Live Streaming (HLS) and Dynamic Adaptive Streaming over HTTP (DASH), within the context of WebOS and Tizen operating systems that dominate the Smart TV market.
This study employs a structured methodology to develop and evaluate a web engine-based video player, addressing key performance challenges such as buffering time reduction, track switching optimization, and adaptive streaming efficiency. The research methodology integrates quantitative performance metrics and qualitative user experience assessments to evaluate the effectiveness of various optimization techniques. The study also emphasizes the integration of Digital Rights Management (DRM) technologies and their impact on playback performance.
The implemented video player demonstrates significant improvements over baseline implementations, with a 42% reduction in initial buffering time, 37% decrease in rebuffering frequency, and 28% more stable quality transitions during network fluctuations. These improvements are achieved through a combination of platform-specific optimizations, buffer management strategies, and adaptive bitrate algorithms tailored for television applications. Results indicate that buffer-based adaptation approaches generally outperform throughput-based algorithms on Smart TV platforms, particularly when optimized for specific rendering engine characteristics.
This research contributes to the field by providing empirical evidence for the effectiveness of specific optimization techniques in television applications and developing a framework for evaluating streaming performance across different Smart TV platforms. The findings have practical implications for application developers, content providers, and platform manufacturers working to improve streaming experiences on Smart TV devices. Future research directions include exploring machine learning-based adaptation strategies and hybrid native/web approaches for further performance enhancements.
This study employs a structured methodology to develop and evaluate a web engine-based video player, addressing key performance challenges such as buffering time reduction, track switching optimization, and adaptive streaming efficiency. The research methodology integrates quantitative performance metrics and qualitative user experience assessments to evaluate the effectiveness of various optimization techniques. The study also emphasizes the integration of Digital Rights Management (DRM) technologies and their impact on playback performance.
The implemented video player demonstrates significant improvements over baseline implementations, with a 42% reduction in initial buffering time, 37% decrease in rebuffering frequency, and 28% more stable quality transitions during network fluctuations. These improvements are achieved through a combination of platform-specific optimizations, buffer management strategies, and adaptive bitrate algorithms tailored for television applications. Results indicate that buffer-based adaptation approaches generally outperform throughput-based algorithms on Smart TV platforms, particularly when optimized for specific rendering engine characteristics.
This research contributes to the field by providing empirical evidence for the effectiveness of specific optimization techniques in television applications and developing a framework for evaluating streaming performance across different Smart TV platforms. The findings have practical implications for application developers, content providers, and platform manufacturers working to improve streaming experiences on Smart TV devices. Future research directions include exploring machine learning-based adaptation strategies and hybrid native/web approaches for further performance enhancements.