Food Waste Reduction Network

Netflix Video Quality Optimization for Slow Networks

Engineering Practice Problem

12 - 16 hours

Build adaptive streaming algorithm for 0.5-3 Mbps connections using predictive network modeling, strategic preloading, and smooth quality transitions. Minimize buffering while maximizing perceived quality through intelligent bitrate decisions.

Food Waste Reduction Network

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Problem Statement

Netflix operates globally, but 30% of users experience inconsistent connectivity, particularly in rural areas or developing countries with connections between 0.5-3 Mbps. Traditional adaptive bitrate (ABR) streaming reacts to network changes, causing frequent quality shifts that frustrate viewers. Netflix needs a system that predicts network conditions using historical patterns, preloads strategic portions of content at different qualities, implements smooth quality transitions, and makes intelligent decisions about when to buffer versus maintain playback. The solution must minimize buffering events while maximizing perceived quality, considering that user preferences vary (some prioritize continuous playback, others want best possible quality) and different content types have different quality requirements (dialogue-heavy shows can tolerate lower bitrates than action sequences).

Submission Guidelines

Build an adaptive video streaming algorithm that provides the best possible viewing experience on low-bandwidth connections. Your solution should go beyond traditional ABR by incorporating prediction, strategic preloading, and user preference. Develop a simulation framework that tests your algorithm against realistic variable network profiles. Compare performance metrics (buffering frequency, rebuffering time, average quality, quality switches) against a baseline ABR algorithm. Document your approach including prediction methods, buffering strategies, and quality selection logic.

Deliverables

Submit ALL of the following:

Working algorithm implementation in Python, JavaScript, or C++
Simulation framework testing against at least 5 different variable network profiles
Performance benchmarks comparing your solution to standard ABR algorithms with graphs
Technical documentation including pseudocode, complexity analysis, and design decisions (4-6 pages)
GitHub repository with comprehensive README, setup instructions, and test cases
Optional but encouraged: Video demonstration showing side-by-side comparison with traditional ABR

This practice problem is suitable for:

Software Engineers

Product Managers

Data Science Roles

Students

Technology Enthusiasts

Judging Criteria

Algorithm design (30%) – Sophistication of prediction models, decision logic, and buffering strategies
Performance metrics (25%) – Measurable reduction in buffering events, quality variance, and improved user experience
Code quality (20%) – Documentation, testing coverage, modularity, and code organization
Network modeling (15%) – Realism of simulation and diversity of test scenarios
Innovation (10%) – Novel approaches to quality optimization beyond existing ABR techniques

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