UAV Communication Bandwidth Optimization for Video Streaming

Adaptive bitrate streaming system that improves video quality by selectively requesting higher bitrate segments for complex scenes based on available bandwidth, while maintaining smooth playback and minimizing buffering. The system determines scene complexity and dynamically adjusts bitrate requests to prioritize high-motion content, enabling better video quality without compromising stability.

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A method for adaptive bitrate streaming that selects a target version of encoded video content based on display properties rather than decoder capabilities, and streams it even when network bandwidth is sufficient to support higher quality versions. The target version is encoded to a target value of an encoding attribute, such as frame size, that is less than the maximum value supported by the client device.

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Predicting bitrate selection for streaming media using machine learning models to improve adaptive bitrate streaming performance. The technology employs supervised learning to train models that predict future download speeds and buffer states, enabling more accurate bitrate selection and reduced rebuffering.

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Adaptive bitrate (ABR) streaming method for video content delivery over HTTP networks, where a receiver requests video segments based on available bandwidth rather than buffer levels. The method involves probing available bandwidth by requesting enhancement layer segments with decreasing priorities, and adjusting subsequent base layer segment requests based on the received enhancement layer segments.

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A streaming assistance system that optimizes media content delivery by dynamically switching between multiple network connections, including satellite and terrestrial links, to aggregate bandwidth and reduce latency. The system identifies media fragments expected to be requested by the client device and prefetches them in parallel using both network links, enabling seamless playback even in areas with limited connectivity.

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A network monitoring system that dynamically establishes bandwidth allocation policies across various network environments through transferable reinforcement learning, enabling smooth operation of quality-of-experience (QoE) centered IoT applications. The system employs reinforcement learning to optimize bandwidth allocation based on real-time network conditions, ensuring QoE meets predefined targets while adapting to changing network environments.

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Predicting bitrate selection for streaming media using machine learning models to improve adaptive bitrate streaming performance. The system employs supervised learning models, such as random forest decision trees, to predict future download speeds and buffer behavior based on historical data and streaming parameters. The models can be integrated into streaming devices or run remotely in the cloud to optimize bitrate selection and prevent rebuffering.

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Adaptive bitrate streaming system that improves video quality by selectively requesting higher bitrate segments for complex scenes, even if the user's available bandwidth is insufficient to support them. The system determines scene complexity and dynamically adjusts bitrate requests to prioritize high-complexity segments, enabling better video quality without compromising playback stability.

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A method for adaptive bitrate streaming of digital video that improves quality selection by analyzing buffer data and download rates in a range-based manner, rather than relying on a single bandwidth estimate. The method segments the video into ranges and evaluates the buffer data and download rates for each range to determine the optimal quality for streaming. This approach enables more accurate quality selection and reduced rebuffering delays compared to traditional adaptive bitrate methods.

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A wireless video transmission system that employs a hierarchical data structure to represent video data, comprising a base layer and one or more enhancement layers with associated residual data. The base layer provides a coarse rendition of the input signal, while the enhancement layers enhance the base layer to achieve high-quality video. The system dynamically adjusts the transmission rate of the enhancement layers based on channel conditions to minimize latency while maintaining a base level of quality.

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A technique to improve multimedia streaming efficiency by dynamically increasing resolution of previously streamed segments without requiring re-streaming. When streaming video, instead of always adjusting bitrate based on available bandwidth, segments are initially streamed at lower resolution. Later segments are streamed at higher resolution if bandwidth allows. To avoid wasting bandwidth if the viewer skips ahead, segments are also streamed at lower resolution than optimal. Later, pixels are added to upgrade resolution only for viewed segments. This allows increasing resolution without re-streaming and prevents wasting bandwidth on skipped segments.

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A system and method for adaptive media playback that improves quality by dynamically adjusting bitrate based on multiple factors beyond network throughput, including decode performance, render performance, player overhead, and system resources. The system continuously monitors playback performance and adjusts bitrate requests to ensure optimal quality, rather than relying solely on network bandwidth.

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Adjusting video transmission rate during segment transmission to prevent packet loss and maintain video quality. When network conditions deteriorate during a segment, the transmission rate is reduced for the entire segment, rather than waiting for the current segment to complete. The client device is instructed to decode and present the lower bitrate segment at a faster rate, skipping the already presented higher bitrate frames.

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Adaptive bitrate streaming system that improves video quality by selectively requesting higher bitrate segments for complex scenes based on available bandwidth, while maintaining smooth playback. The system determines scene complexity and dynamically adjusts bitrate requests to prioritize high-motion content, enabling better video quality without compromising stability.

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A remote video transmission method that dynamically adjusts encoding bit rate based on client bandwidth and packet loss, and coordinates server and client capabilities to achieve smooth and high-quality video transmission. The method includes the server sending video frames to the client, receiving client feedback on packet loss and bandwidth, predicting its own bandwidth, and adjusting its encoding bit rate accordingly. The server also monitors client decoding and rendering capabilities and adjusts its frame rate accordingly.

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Optimizing video streaming to reduce stalls and improve playback in adaptive bitrate (ABR) video. The method involves prioritizing frame transmission order within a video chunk. Frames are ordered with I-frames first, followed by forward-predicted P-frames, then reference B-frames, and finally optional non-reference b-frames. This prioritization ensures critical reference frames are received first, even if the chunk is truncated before all frames. If a stall risk is detected during chunk download, the player requests the next chunk and stops the current one mid-transfer. This allows using in-flight frames to extend playback without waiting for the full chunk.

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A method for dynamically adapting video streaming profiles based on predicted network conditions. The method involves predicting network conditions using session features, selecting a subset of available profiles based on the predicted conditions, and providing a profile ladder that restricts the client to using the selected subset of profiles. The profile ladder is dynamically updated based on playback conditions during the session.

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Synchronizing playback of media content across multiple devices to prevent buffering delays and quality issues. The method involves coordinating the streaming rates to match device buffer speeds. If a device falls behind, the server lowers the quality of the stream to match. This ensures all devices stay synchronized during playback.

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A system and method for managing video streaming traffic in a network to maintain Quality of Experience (QoE) across multiple service categories. The system determines a set of service categories, maps traffic flows to each category, and measures QoE and bandwidth use. It adjusts minimum bandwidth intents for categories not meeting their targets, allocates bandwidth based on the adjusted intents, and shapes traffic flows to maintain QoE across categories. The system manages video streaming traffic growth to prevent congestion while maintaining acceptable QoE for other service categories.

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Adaptive streaming service for multiple narrowband wireless networks that dynamically selects between TCP and UDP protocols and determines redundant data sizes based on network conditions to provide stable and energy-efficient video streaming in environments with limited bandwidth and packet loss.

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