# Towards a New Business Model for Streaming Platforms Using Blockchain Technology
## 4 Proposed Solution
### Automatic Smart Contract Generation Service
In Figure 4, the bottom part displays the digital wallet addresses of the streaming platform and the content creator, along with each wallet's Ethereum (ETH) balance. During the assessment, the contract published on

Figure 4: Streaming platform using blockchain technology. the Ethereum network processed nine videos and released payments accordingly. Once the videos receive five hundred views, the contract manages the payment between the streaming platform and the content creator. In addition to the ethical and legal aspects, transparency allows parties involved in a business model to consult the terms and clauses of the signed agreement. In this aspect, StreamingChain allows stakeholders engaged in the business model to determine whether data transactions comply with the deal. For example, if a content creator who received twenty payments in his digital wallet wants to check how the streaming platform calculated the amounts paid, he can consult the transactions that make up the amount spent on the blockchain network. Figure 5 illustrates how StreamingChain enables content creators to track the transactions that lead to a received payment. The example in Figure 5 depicts a contract published on the Fantom network, resulting from assessing one of the videos included in this evaluation. In the scenario illustrated above, a content creator has received a payment of FTM 1 for their content. To trace the origin of the transactions that gave rise to this payment, the following steps need to be taken: Access the contract's address on the blockchain network and select the internal transactions option to view the transaction hash, block number, transaction date, origin of the transaction, wallet address that received the transfer, and the amount that was transferred. This way, content creators can verify the origin of the views and payments they receive from the streaming platform. Finally, the financial cost of processing transactions on blockchain networks was investigated. Using the ETH cryptocurrency as a reference in the Ethereum network infrastructure was based on a market price of USD 1891.49 per unit of ETH, while the gas price was set at 20 Gwei. In this context, the average transaction processing cost was USD 00.97, resulting in a total cost of USD 9742.76. On the other hand, in the Fantom network, the reference cryptocurrency was FTM, whose market price was USD 00.32 per unit of FTM, and the gas price was set at 3.5 Gwei. Under these conditions, the average cost of processing transactions was equivalent to USD 0.0001, resulting in a total cost of

Figure 5: Viewing the Fantom network contract. USD 01.62. Like the Ethereum network, the price of gas on the Fantom network played an essential role in determining operating costs and is vital to a comprehensive understanding of the financial aspects inherent in executing transactions. Table 2 shows the results presented above. ### Monitoring Hardware and Software Resources

To complement our first evaluation, we conducted a second experiment to investigate the processing of StreamingChain data requests on the Fantom and Ethereum networks using the proof of concept developed. In this evaluation, we have two main objectives: Firstly, to monitor the hardware resources consumed during the execution of a set of transactions, such as processor usage, RAM, and processing stack. Then, to inspect the flow of web requests made by the software application that simulates the video streaming platform and StreamingChain. This made it possible to measure successful and failed transactions, request latency, and the amount of gas used to process the request on the blockchain network. Individually, 20,000 transactions were executed on each blockchain network: 12,000 for audience metrics updates (Update), 7800 for data visualizations (Get), and 200 for the creation of new video contracts (Post). This data load distribution represents suggestions recommended by data persistence benchmarks to simulate data update, query, and creation operations.